air quality in china case study

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• Published: 29 October 2020

Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

• Lu Liang 1 &
• Peng Gong 2 , 3 , 4  

Scientific Reports volume  10 , Article number:  18618 ( 2020 ) Cite this article

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Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM 2.5 ) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM 2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM 2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM 2.5 level increases. We suggest that, depending on the city’s developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.

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Introduction.

Air pollution represents a prominent threat to global society by causing cascading effects on individuals 1 , medical systems 2 , ecosystem health 3 , and economies 4 in both developing and developed countries 5 , 6 , 7 , 8 . About 90% of global citizens lived in areas that exceed the safe level in the World Health Organization (WHO) air quality guidelines 9 . Among all types of ecosystems, urban produce roughly 78% of carbon emissions and substantial airborne pollutants that adversely affect over 50% of the world’s population living in them 5 , 10 . While air pollution affects all regions, there exhibits substantial regional variation in air pollution levels 11 . For instance, the annual mean concentration of fine particulate matter with an aerodynamic diameter of less than 2.5  \(\upmu\mathrm{m}\) (PM 2.5 ) in the most polluted cities is nearly 20 times higher than the cleanest city according to a survey of 499 global cities 12 . Many factors can influence the regional air quality, including emissions, meteorology, and physicochemical transformations. Another non-negligible driver is urbanization—a process that alters the size, structure, and growth of cities in response to the population explosion and further leads to lasting air quality challenges 13 , 14 , 15 .

With the global trend of urbanization 16 , the spatial composition, configuration, and density of urban land uses (refer to as urban form) will continue to evolve 13 . The investigation of urban form impacts on air quality has been emerging in both empirical 17 and theoretical 18 research. While the area and density of artificial surface areas have well documented positive relationship with air pollution 19 , 20 , 21 , the effects of urban fragmentation on air quality have been controversial. In theory, compact cities promote high residential density with mixed land uses and thus reduce auto dependence and increase the usage of public transit and walking 21 , 22 . The compact urban development has been proved effective in mitigating air pollution in some cities 23 , 24 . A survey of 83 global urban areas also found that those with highly contiguous built-up areas emitted less NO 2 22 . In contrast, dispersed urban form can decentralize industrial polluters, improve fuel efficiency with less traffic congestion, and alleviate street canyon effects 25 , 26 , 27 , 28 . Polycentric and dispersed cities support the decentralization of jobs that lead to less pollution emission than compact and monocentric cities 29 . The more open spaces in a dispersed city support air dilution 30 . In contrast, compact cities are typically associated with stronger urban heat island effects 31 , which influence the availability and the advection of primary and secondary pollutants 32 .

The mixed evidence demonstrates the complex interplay between urban form and air pollution, which further implies that the inconsistent relationship may exist in cities at different urbanization levels and over different periods 33 . Few studies have attempted to investigate the urban form–air pollution relationship with cross-sectional and time series data 34 , 35 , 36 , 37 . Most studies were conducted in one city or metropolitan region 38 , 39 or even at the country level 40 . Furthermore, large cities or metropolitan areas draw the most attention in relevant studies 5 , 41 , 42 , and the small- and mid-sized cities, especially those in developing countries, are heavily underemphasized. However, virtually all world population growth 43 , 44 and most global economic growth 45 , 46 are expected to occur in those cities over the next several decades. Thus, an overlooked yet essential task is to account for various levels of cities, ranging from large metropolitan areas to less extensive urban area, in the analysis.

This study aims to improve the understanding of how the urban form evolution explains the decadal-long changes of the annual mean PM 2.5 concentrations in 626 cities at the county-level and above in China. China has undergone unprecedented urbanization over the past few decades and manifested a high degree of heterogeneity in urban development 47 . Thus, Chinese cities serve as a good model for addressing the following questions: (1) whether the changes in urban landscape patterns affect trends in PM 2.5 levels? And (2) if so, do the determinants vary by cities?

City boundaries

Our study period spans from the year 2000 to 2014 to keep the data completeness among all data sources. After excluding cities with invalid or missing PM 2.5 or sociodemographic value, a total of 626 cities, with 278 prefecture-level cities and 348 county-level cities, were selected. City boundaries are primarily based on the Global Rural–Urban Mapping Project (GRUMP) urban extent polygons that were defined by the extent of the nighttime lights 48 , 49 . Few adjustments were made. First, in the GRUMP dataset, large agglomerations that include several cities were often described in one big polygon. We manually split those polygons into individual cities based on the China Administrative Regions GIS Data at 1:1 million scales 50 . Second, since the 1978 economic reforms, China has significantly restructured its urban administrative/spatial system. Noticeable changes are the abolishment of several prefectures and the promotion of many former county-level cities to prefecture-level cities 51 . Thus, all city names were cross-checked between the year 2000 and 2014, and the mismatched records were replaced with the latest names.

PM 2.5 concentration data

The annual mean PM 2.5 surface concentration (micrograms per cubic meter) for each city over the study period was calculated from the Global Annual PM 2.5 Grids at 0.01° resolution 52 . This data set combines Aerosol Optical Depth retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The global 3-D chemical transport model GEOS-Chem is further applied to relate this total column measure of aerosol to near-surface PM 2.5 concentration, and geographically weighted regression is finally used with global ground-based measurements to predict and adjust for the residual PM 2.5 bias per grid cell in the initial satellite-derived values.

Human settlement layer

The urban forms were quantified with the 40-year (1978–2017) record of annual impervious surface maps for both rural and urban areas in China 47 , 53 . This state-of-art product provides substantial spatial–temporal details on China’s human settlement changes. The annual impervious surface maps covering our study period were generated from 30-m resolution Landsat images acquired onboard Landsat 5, 7, and 8 using an automatic “Exclusion/Inclusion” mapping framework 54 , 55 . The output used here was the binary impervious surface mask, with the value of one indicating the presence of human settlement and the value of zero identifying non-residential areas. The product assessment concluded good performance. The cross-comparison against 2356 city or town locations in GeoNames proved an overall high agreement (88%) and approximately 80% agreement was achieved when compared against visually interpreted 650 urban extent areas in the year 1990, 2000, and 2010.

Control variables

To provide a holistic assessment of the urban form effects, we included control variables that are regarded as important in influencing air quality to account for the confounding effects.

Four variables, separately population size, population density, and two economic measures, were acquired from the China City Statistical Yearbook 56 (National Bureau of Statistics 2000–2014). Population size is used to control for the absolute level of pollution emissions 41 . Larger populations are associated with increased vehicle usage and vehicle-kilometers travels, and consequently boost tailpipes emissions 5 . Population density is a useful reflector of transportation demand and the fraction of emissions inhaled by people 57 . We also included gross regional product (GRP) and the proportion of GRP generated from the secondary sector (GRP2). The impact of economic development on air quality is significant but in a dynamic way 58 . The rising per capita income due to the concentration of manufacturing industrial activities can deteriorate air quality and vice versa if the stronger economy is the outcome of the concentration of less polluting high-tech industries. Meteorological conditions also have short- and long-term effects on the occurrence, transport, and dispersion of air pollutants 59 , 60 , 61 . Temperature affects chemical reactions and atmospheric turbulence that determine the formation and diffusion of particles 62 . Low air humidity can lead to the accumulation of air pollutants due to it is conducive to the adhesion of atmospheric particulate matter on water vapor 63 . Whereas high humidity can lead to wet deposition processes that can remove air pollutants by rainfall. Wind speed is a crucial indicator of atmospheric activity by greatly affect air pollutant transport and dispersion. All meteorological variables were calculated based on China 1 km raster layers of monthly relative humidity, temperature, and wind speed that are interpolated from over 800 ground monitoring stations 64 . Based on the monthly layer, we calculated the annual mean of each variable for each year. Finally, all pixels falling inside of the city boundary were averaged to represent the overall meteorological condition of each city.

Considering the dynamic urban form-air pollution relationship evidenced from the literature review, our hypothesis is: the determinants of PM 2.5 level trends are not the same for cities undergoing different levels of development or in different geographic regions. To test this hypothesis, we first categorized city groups following (1) social-economic development level, (2) spatial autocorrelation relationship, and (3) population size. We then assessed the relationship between urban form and PM 2.5 level trends by city groups. Finally, we applied the panel data models to different city groups for hypothesis testing and key determinant identification (Fig.  1 ).

Methodology workflow.

Calculation of urban form metrics

Based on the previous knowledge 65 , 66 , 67 , fifteen landscape metrics falling into three categories, separately area, shape, and aggregation, were selected. Those metrics quantify the compositional and configurational characteristics of the urban landscape, as represented by urban expansion, urban shape complexity, and compactness (Table 1 ).

Area metrics gives an overview of the urban extent and the size of urban patches that are correlated with PM 2.5 20 . As an indicator of the urbanization degree, total area (TA) typically increases constantly or remains stable, because the urbanization process is irreversible. Number of patches (NP) refers to the number of discrete parcels of urban settlement within a given urban extent and Mean Patch Size (AREA_MN) measures the average patch size. Patch density (PD) indicates the urbanization stages. It usually increases with urban diffusion until coalescence starts, after which decreases in number 66 . Largest Patch Index (LPI) measures the percentage of the landscape encompassed by the largest urban patch.

The shape complexity of urban patches was represented by Mean Patch Shape Index (SHAPE_MN), Mean Patch Fractal Dimension (FRAC_MN), and Mean Contiguity Index (CONTIG_MN). The greater irregularity the landscape shape, the larger the value of SHAPE_MN and FRAC_MN. CONTIG_MN is another method of assessing patch shape based on the spatial connectedness or contiguity of cells within a patch. Larger contiguous patches will result in larger CONTIG_MN.

Aggregation metrics measure the spatial compactness of urban land, which affects pollutant diffusion and dilution. Mean Euclidean nearest-neighbor distance (ENN_MN) quantifies the average distance between two patches within a landscape. It decreases as patches grow together and increases as the urban areas expand. Landscape Shape Index (LSI) indicates the divergence of the shape of a landscape patch that increases as the landscape becomes increasingly disaggregated 68 . Patch Cohesion Index (COHESION) is suggestive of the connectedness degree of patches 69 . Splitting Index (SPLIT) and Landscape Division Index (DIVISION) increase as the separation of urban patches rises, whereas, Mesh Size (MESH) decreases as the landscape becomes more fragmented. Aggregation Index (AI) measures the degree of aggregation or clumping of urban patches. Higher values of continuity indicate higher building densities, which may have a stronger effect on pollution diffusion.

The detailed descriptions of these indices are given by the FRAGSTATS user’s guide 70 . The calculation input is a layer of binary grids of urban/nonurban. The resulting output is a table containing one row for each city and multiple columns representing the individual metrics.

Division of cities

Division based on the socioeconomic development level.

The socioeconomic development level in China is uneven. The unequal development of the transportation system, descending in topography from the west to the east, combined with variations in the availability of natural and human resources and industrial infrastructure, has produced significantly wide gaps in the regional economies of China. By taking both the economic development level and natural geography into account, China can be loosely classified into Eastern, Central, and Western regions. Eastern China is generally wealthier than the interior, resulting from closeness to coastlines and the Open-Door Policy favoring coastal regions. Western China is historically behind in economic development because of its high elevation and rugged topography, which creates barriers in the transportation infrastructure construction and scarcity of arable lands. Central China, echoing its name, is in the process of economic development. This region neither benefited from geographic convenience to the coast nor benefited from any preferential policies, such as the Western Development Campaign.

Division based on spatial autocorrelation relationship

The second type of division follows the fact that adjacent cities are likely to form air pollution clusters due to the mixing and diluting nature of air pollutants 71 , i.e., cities share similar pollution levels as its neighbors. The underlying processes driving the formation of pollution hot spots and cold spots may differ. Thus, we further divided the city into groups based on the spatial clusters of PM 2.5 level changes.

Local indicators of spatial autocorrelation (LISA) was used to determine the local patterns of PM 2.5 distribution by clustering cities with a significant association. In the presence of global spatial autocorrelation, LISA indicates whether a variable exhibits significant spatial dependence and heterogeneity at a given scale 72 . Practically, LISA relates each observation to its neighbors and assigns a value of significance level and degree of spatial autocorrelation, which is calculated by the similarity in variable \(z\) between observation \(i\) and observation \(j\) in the neighborhood of \(i\) defined by a matrix of weights \({w}_{ij}\) 7 , 73 :

where \({I}_{i}\) is the Moran’s I value for location \(i\) ; \({\sigma }^{2}\) is the variance of variable \(z\) ; \(\bar{z}\) is the average value of \(z\) with the sample number of \(n\) . The weight matrix \({w}_{ij}\) is defined by the k-nearest neighbors distance measure, i.e., each object’s neighborhood consists of four closest cites.

The computation of Moran’s I enables the identification of hot spots and cold spots. The hot spots are high-high clusters where the increase in the PM 2.5 level is higher than the surrounding areas, whereas cold spots are low-low clusters with the presence of low values in a low-value neighborhood. A Moran scatterplot, with x-axis as the original variable and y-axis as the spatially lagged variable, reflects the spatial association pattern. The slope of the linear fit to the scatter plot is an estimation of the global Moran's I 72 (Fig.  2 ). The plot consists of four quadrants, each defining the relationship between an observation 74 . The upper right quadrant indicates hot spots and the lower left quadrant displays cold spots 75 .

Moran’s I scatterplot. Figure was produced by R 3.4.3 76 .

Division based on population size

The last division was based on population size, which is a proven factor in changing per capita emissions in a wide selection of global cities, even outperformed land urbanization rate 77 , 78 , 79 . We used the 2014 urban population to classify the cities into four groups based on United Nations definitions 80 : (1) large agglomerations with a total population larger than 1 million; (2) mid-sized cities, 500,000–1 million; (3) small cities, 250,000–500,000, and (4) very small cities, 100,000–250,000.

Panel data analysis

The panel data analysis is an analytical method that deals with observations from multiple entities over multiple periods. Its capacity in analyzing the characteristics and changes from both the time-series and cross-section dimensions of data surpasses conventional models that purely focus on one dimension 81 , 82 . The estimation equation for the panel data model in this study is given as:

where the subscript \(i\) and \(t\) refer to city and year respectively. \(\upbeta _{{0}}\) is the intercept parameter and \(\upbeta _{{1}} - { }\upbeta _{{{18}}}\) are the estimates of slope coefficients. \(\varepsilon \) is the random error. All variables are transformed into natural logarithms.

Two methods can be used to obtain model estimates, separately fixed effects estimator and random effects estimator. The fixed effects estimator assumes that each subject has its specific characteristics due to inherent individual characteristic effects in the error term, thereby allowing differences to be intercepted between subjects. The random effects estimator assumes that the individual characteristic effect changes stochastically, and the differences in subjects are not fixed in time and are independent between subjects. To choose the right estimator, we run both models for each group of cities based on the Hausman specification test 83 . The null hypothesis is that random effects model yields consistent and efficient estimates 84 : \({H}_{0}{:}\,E\left({\varepsilon }_{i}|{X}_{it}\right)=0\) . If the null hypothesis is rejected, the fixed effects model will be selected for further inferences. Once the better estimator was determined for each model, one optimal panel data model was fit to each city group of one division type. In total, six, four, and eight runs were conducted for socioeconomic, spatial autocorrelation, and population division separately and three, two, and four panel data models were finally selected.

Spatial patterns of PM 2.5 level changes

During the period from 2000 to 2014, the annual mean PM 2.5 concentration of all cities increases from 27.78 to 42.34 µg/m 3 , both of which exceed the World Health Organization recommended annual mean standard (10 µg/m 3 ). It is worth noting that the PM 2.5 level in the year 2014 also exceeds China’s air quality Class 2 standard (35 µg/m 3 ) that applies to non-national park places, including urban and industrial areas. The standard deviation of annual mean PM 2.5 values for all cities increases from 12.34 to 16.71 µg/m 3 , which shows a higher variability of inter-urban PM 2.5 pollution after a decadal period. The least and most heavily polluted cities in China are Delingha, Qinghai (3.01 µg/m 3 ) and Jizhou, Hubei (64.15 µg/m 3 ) in 2000 and Hami, Xinjiang (6.86 µg/m 3 ) and Baoding, Hubei (86.72 µg/m 3 ) in 2014.

Spatially, the changes in PM 2.5 levels exhibit heterogeneous patterns across cities (Fig.  3 b). According to the socioeconomic level division (Fig.  3 a), the Eastern, Central, and Western region experienced a 38.6, 35.3, and 25.5 µg/m 3 increase in annual PM 2.5 mean , separately, and the difference among regions is significant according to the analysis of variance (ANOVA) results (Fig.  4 a). When stratified by spatial autocorrelation relationship (Fig.  3 c), the differences in PM 2.5 changes among the spatial clusters are even more dramatic. The average PM 2.5 increase in cities belonging to the high-high cluster is approximately 25 µg/m 3 , as compared to 5 µg/m 3 in the low-low clusters (Fig.  4 b). Finally, cities at four different population levels have significant differences in the changes of PM 2.5 concentration (Fig.  3 d), except for the mid-sized cities and large city agglomeration (Fig.  4 c).

( a ) Division of cities in China by socioeconomic development level and the locations of provincial capitals; ( b ) Changes in annual mean PM 2.5 concentrations between the year 2000 and 2014; ( c ) LISA cluster maps for PM 2.5 changes at the city level; High-high indicates a statistically significant cluster of high PM 2.5 level changes over the study period. Low-low indicates a cluster of low PM 2.5 inter-annual variation; No high-low cluster is reported; Low–high represents cities with high PM 2.5 inter-annual variation surrounded by cities with low variation; ( d ) Population level by cities in the year 2014. Maps were produced by ArcGIS 10.7.1 85 .

Boxplots of PM 2.5 concentration changes between 2000 and 2014 for city groups that are formed according to ( a ) socioeconomic development level division, ( b ) LISA clusters, and ( c ) population level. Asterisk marks represent the p value of ANOVA significant test between the corresponding pair of groups. Note ns not significant; * p value < 0.05; ** p value < 0.01; *** p value < 0.001; H–H high-high cluster, L–H low–high cluster, L–L denotes low–low cluster.

The effects of urban forms on PM 2.5 changes

The Hausman specification test for fixed versus random effects yields a p value less than 0.05, suggesting that the fixed effects model has better performance. We fit one panel data model to each city group and built nine models in total. All models are statistically significant at the p  < 0.05 level and have moderate to high predictive power with the R 2 values ranging from 0.63 to 0.95, which implies that 63–95% of the variation in the PM 2.5 concentration changes can be explained by the explanatory variables (Table 2 ).

The urban form—PM 2.5 relationships differ distinctly in Eastern, Central, and Western China. All models reach high R 2 values. Model for Eastern China (refer to hereafter as Eastern model) achieves the highest R 2 (0.90), and the model for the Western China (refer to hereafter as Western model) reaches the lowest R 2 (0.83). The shape metrics FRAC and CONTIG are correlated with PM 2.5 changes in the Eastern model, whereas the area metrics AREA demonstrates a positive effect in the Western model. In contrast to the significant associations between shape, area metrics and PM 2.5 level changes in both Eastern and Western models, no such association was detected in the Central model. Nonetheless, two aggregation metrics, LSI and AI, play positive roles in determining the PM 2.5 trends in the Central model.

For models built upon the LISA clusters, the H–H model (R 2  = 0.95) reaches a higher fitting degree than the L–L model (R 2  = 0.63). The estimated coefficients vary substantially. In the H–H model, the coefficient of CONTIG is positive, which indicates that an increase in CONTIG would increase PM 2.5 pollution. In contrast, no shape metrics but one area metrics AREA is significant in the L–L model.

The results of the regression models built for cities at different population levels exhibit a distinct pattern. No urban form metrics was identified to have a significant relationship with the PM 2.5 level changes in groups of very small and mid-sized cities. For small size cities, the aggregation metrics COHESION was positively associated whereas AI was negatively related. For mid-sized cities and large agglomerations, CONTIG is the only significant variable that is positively related to PM 2.5 level changes.

Urban form is an effective measure of long-term PM 2.5 trends

All panel data models are statistically significant regardless of the data group they are built on, suggesting that the associations between urban form and ambient PM 2.5 level changes are discernible at all city levels. Importantly, these relationships are found to hold when controlling for population size and gross domestic product, implying that the urban landscape patterns have effects on long-term PM 2.5 trends that are independent of regional economic performance. These findings echo with the local, regional, and global evidence of urban form effect on various air pollution types 5 , 14 , 21 , 22 , 24 , 39 , 78 .

Although all models demonstrate moderate to high predictive power, the way how different urban form metrics respond to the dependent variable varies. Of all the metrics tested, shape metrics, especially CONTIG has the strongest effect on PM 2.5 trends in cities belonging to the high-high cluster, Eastern, and large urban agglomerations. All those regions have a strong economy and higher population density 86 . In the group of cities that are moderately developed, such as the Central region, as well as small- and mid-sized cities, aggregation metrics play a dominant negative role in PM 2.5 level changes. In contrast, in the least developed cities belonging to the low-low cluster regions and Western China, the metrics describing size and number of urban patches are the strongest predictors. AREA and NP are positively related whereas TA is negatively associated.

The impacts of urban form metrics on air quality vary by urbanization degree

Based on the above observations, how urban form affects within-city PM 2.5 level changes may differ over the urbanization stages. We conceptually summarized the pattern in Fig.  5 : area metrics have the most substantial influence on air pollution changes at the early urban development stage, and aggregation metrics emerge at the transition stage, whereas shape metrics affect the air quality trends at the terminal stage. The relationship between urban form and air pollution has rarely been explored with such a wide range of city selections. Most prior studies were focused on large urban agglomeration areas, and thus their conclusions are not representative towards small cities at the early or transition stage of urbanization.

The most influential metric of urban form in affecting PM 2.5 level changes at different urbanization stages.

Not surprisingly, the area metrics, which describe spatial grain of the landscape, exert a significant effect on PM 2.5 level changes in small-sized cities. This could be explained by the unusual urbanization speed of small-sized cities in the Chinese context. Their thriving mostly benefited from the urbanization policy in the 1980s, which emphasized industrialization of rural, small- and mid-sized cities 87 . With the large rural-to-urban migration and growing public interest in investing real estate market, a side effect is that the massive housing construction that sometimes exceeds market demand. Residential activities decline in newly built areas of smaller cities in China, leading to what are known as ghost cities 88 . Although ghost cities do not exist for all cities, high rate of unoccupied dwellings is commonly seen in cities under the prefectural level. This partly explained the negative impacts of TA on PM 2.5 level changes, as an expanded while unoccupied or non-industrialized urban zones may lower the average PM 2.5 concentration within the city boundary, but it doesn’t necessarily mean that the air quality got improved in the city cores.

Aggregation metrics at the landscape scale is often referred to as landscape texture that quantifies the tendency of patch types to be spatially aggregated; i.e., broadly speaking, aggregated or “contagious” distributions. This group of metrics is most effective in capturing the PM 2.5 trends in mid-sized cities (population range 25–50 k) and Central China, where the urbanization process is still undergoing. The three significant variables that reflect the spatial property of dispersion, separately landscape shape index, patch cohesion index, and aggregation index, consistently indicate that more aggregated landscape results in a higher degree of PM 2.5 level changes. Theoretically, the more compact urban form typically leads to less auto dependence and heavier reliance on the usage of public transit and walking, which contributes to air pollution mitigation 89 . This phenomenon has also been observed in China, as the vehicle-use intensity (kilometers traveled per vehicle per year, VKT) has been declining over recent years 90 . However, VKT only represents the travel intensity of one car and does not reflect the total distance traveled that cumulatively contribute to the local pollution. It should be noted that the private light-duty vehicle ownership in China has increased exponentially and is forecast to reach 23–42 million by 2050, with the share of new-growth purchases representing 16–28% 90 . In this case, considering the increased total distance traveled, the less dispersed urban form can exert negative effects on air quality by concentrating vehicle pollution emissions in a limited space.

Finally, urban contiguity, observed as the most effective shape metric in indicating PM 2.5 level changes, provides an assessment of spatial connectedness across all urban patches. Urban contiguity is found to have a positive effect on the long-term PM 2.5 pollution changes in large cities. Urban contiguity reflects to which degree the urban landscape is fragmented. Large contiguous patches result in large CONTIG_MN values. Among the 626 cities, only 11% of cities experience negative changes in urban contiguity. For example, Qingyang, Gansu is one of the cities-featuring leapfrogs and scattered development separated by vacant land that may later be filled in as the development continues (Fig.  6 ). Most Chinese cities experienced increased urban contiguity, with less fragmented and compacted landscape. A typical example is Shenzhou, Hebei, where CONTIG_MN rose from 0.27 to 0.45 within the 14 years. Although the 13 counties in Shenzhou are very far scattered from each other, each county is growing intensively internally rather than sprawling further outside. And its urban layout is thus more compact (Fig.  6 ). The positive association revealed in this study contradicts a global study indicating that cities with highly contiguous built-up areas have lower NO 2 pollution 22 . We noticed that the principal emission sources of NO 2 differ from that of PM 2.5. NO 2 is primarily emitted with the combustion of fossil fuels (e.g., industrial processes and power generation) 6 , whereas road traffic attributes more to PM 2.5 emissions. Highly connected urban form is likely to cause traffic congestion and trap pollution inside the street canyon, which accumulates higher PM 2.5 concentration. Computer simulation results also indicate that more compact cities improve urban air quality but are under the premise that mixed land use should be presented 18 . With more connected impervious surfaces, it is merely impossible to expect increasing urban green spaces. If compact urban development does not contribute to a rising proportion of green areas, then such a development does not help mitigating air pollution 41 .

Six cities illustrating negative to positive changes in CONTIG_MN and AREA_MN. Pixels in black show the urban areas in the year 2000 and pixels in red are the expanded urban areas from the year 2000 to 2014. Figure was produced by ArcGIS 10.7.1 85 .

Conclusions

This study explores the regional land-use patterns and air quality in a country with an extraordinarily heterogeneous urbanization pattern. Our study is the first of its kind in investigating such a wide range selection of cities ranging from small-sized ones to large metropolitan areas spanning a long time frame, to gain a comprehensive insight into the varying effects of urban form on air quality trends. And the primary insight yielded from this study is the validation of the hypothesis that the determinants of PM 2.5 level trends are not the same for cities at various developmental levels or in different geographic regions. Certain measures of urban form are robust predictors of air quality trends for a certain group of cities. Therefore, any planning strategy aimed at reducing air pollution should consider its current development status and based upon which, design its future plan. To this end, it is also important to emphasize the main shortcoming of this analysis, which is generally centered around the selection of control variables. This is largely constrained by the available information from the City Statistical Yearbook. It will be beneficial to further polish this study by including other important controlling factors, such as vehicle possession.

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Acknowledgements

Lu Liang received intramural research funding support from the UNT Office of Research and Innovation. Peng Gong is partially supported by the National Research Program of the Ministry of Science and Technology of the People’s Republic of China (2016YFA0600104), and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University.

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Liang, L., Gong, P. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep 10 , 18618 (2020). https://doi.org/10.1038/s41598-020-74524-9

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Effects of urban form on air quality: A case study from China comparing years with normal and reduced human activity due to the COVID-19 pandemic

This study explored the dynamic and complex relationships between air quality and urban form when considering reduced human activities. Applying the random forest method to data from 62 prefecture-level cities in China, urban form–air quality relationships were compared between 2015 (a normal year) and 2020 (which had significantly reduced air pollution due to COVID-19 lockdowns). Significant differences were found between these two years; urban compactness, shape, and size were of prime importance to air quality in 2020, while fragmentation was the most critical factor in improving air quality in 2015. An important influence of traffic mode was also found when controlling air pollution. In general, in the pursuit of reducing air pollution across society, the best urban forms are continuous and compact with reasonable building layouts, population, and road densities, and high forest area ratios. A polycentric urban form that alleviates the negative impacts of traffic pollution is preferable. Urban development should aim to reduce air pollution, and optimizing the effects of urban form on air quality is a cost-effective way to create better living environments. This study provides a reference for decision-makers evaluating the effects of urban form on air pollution emission, dispersion, and concentration in the post-pandemic era.

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Impact of energy structure adjustment on air quality: a case study in Beijing, China

• Research Article
• Published: 02 August 2011
• Volume 5 , pages 378–390, ( 2011 )

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• Bin Zhao 1 ,
• Jiayu Xu 1 &
• Jiming Hao 1  

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Energy consumption is a major cause of air pollution in Beijing, and the adjustment of the energy structure is of strategic importance to the reduction of carbon intensity and the improvement of air quality. In this paper, we explored the future trend of energy structure adjustment in Beijing till 2020, designed five energy scenarios focusing on the fuel substitution in power plants and heating sectors, established emission inventories, and utilized the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) to evaluate the impact of these measures on air quality. By implementing this systematic energy structure adjustment, the emissions of PM 10 , PM 2.5 , SO 2 , NO x , and non-methane volatile organic compounds (NMVOCs) will decrease distinctly by 34.0%, 53.2%, 78.3%, 47.0%, and 30.6% respectively in the most coalintensive scenario of 2020 compared with 2005. Correspondingly, MM5-Models-3/CMAQ simulations indicate significant reduction in the concentrations of major pollutants, implying that energy structure adjustment can play an important role in improving Beijing’s air quality. By fuel substitution for power plants and heating boilers, PM 10 , PM 2.5 , SO 2 , NO x , and NMVOCs will be reduced further, but slightly by 1.7%, 4.5%, 11.4%, 13.5%, and 8.8% respectively in the least coal-intensive scenario. The air quality impacts of different scenarios in 2020 resemble each other, indicating that the potential of air quality improvement due to structure adjustment in power plants and heating sectors is limited. However, the CO 2 emission is 10.0% lower in the least coal-intensive scenario than in the most coal-intensive one, contributing to Beijing’s ambition to build a low carbon city. Except for energy structure adjustment, it is necessary to take further measures to ensure the attainment of air quality standards.

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Zhao, B., Xu, J. & Hao, J. Impact of energy structure adjustment on air quality: a case study in Beijing, China. Front. Environ. Sci. Eng. China 5 , 378–390 (2011). https://doi.org/10.1007/s11783-011-0357-8

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DOI : https://doi.org/10.1007/s11783-011-0357-8

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Improving urban air quality in China: Beijing case study

Affiliation.

• 1 Department of Environmental Science and Engineering, Tsinghua University, Beijing, China.
• PMID: 16259425
• DOI: 10.1080/10473289.2005.10464726

China is undergoing rapid urbanization because of unprecedented economic growth. As a result, many cities suffer from air pollution. Two-thirds of China's cities have not attained the ambient air quality standards applicable to urban residential areas (Grade II). Particulate matter (PM), rather than sulfur dioxide (SO2), is the major pollutant reflecting the shift from coal burning to mixed source pollution. In 2002, 63.2 and 22.4% of the monitored cities have PM and SO2 concentrations exceeding the Grade II standard, respectively. Nitrogen oxides (NOx) concentration kept a relatively stable level near the Grade II standard in the last decade and had an increasing potential in recent years because of the rapid motorization. In general, the air pollutants emission did not increase as quickly as the economic growth and energy consumption, and air quality in Chinese cities has improved to some extent. Beijing, a typical representative of rapidly developing cities, is an example to illustrate the possible options for urban air pollution control. Beijing's case provides hope that the challenges associated with improving air quality can be met during a period of explosive development and motorization.

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• Air Pollutants / analysis*
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• Air Pollution / analysis
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• Particulate Matter
• Population Density
• Power Plants
• Sulfur Dioxide / analysis*
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• Urbanization
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China Air Pollution Data Center launched to combat evolving complexity of air quality challenges

by Chinese Academy of Sciences

While significant strides have been made in improving air quality in China through regulations like the Clean Air Act issued in 2013, air pollution has become increasingly complex. Despite notable improvements, the development of the economy and expansion of vehicular activity have given rise to new challenges, such as the emergence of ozone (O 3 ) pollution, complicating the landscape of air quality management.

In response, a dedicated air pollution data center has been launched, supported by a Major Research Plan of National Natural Science Foundation of China (NSFC) titled "Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China." This initiative aims to delve into the formation mechanisms of air pollution, crucial chemical and physical processes, and their interconnectedness.

This Major Research Plan, comprising 76 individual research projects, has yielded extensive and high-quality data. To consolidate and disseminate these findings for the benefit of scientific research on air pollution, a comprehensive data sharing platform was initiated in 2020.

Spearheaded by Peking University, in collaboration with Tsinghua University, the Institute of Atmospheric Physics of the Chinese Academy of Sciences, Beijing Normal University, and 3Clear Science & Technology Co., Ltd., this platform marks the inception of the China Air Pollution Data Center (CAPDC).

Accessible at www.capdatabase.cn , CAPDC represents the first-ever data sharing platform focused specifically on atmospheric pollution complexities in China. Designed to be inclusive, the platform welcomes both domestic and international scientists.

The introduction of CAPDC has been featured in the journal Advances in Atmospheric Sciences , categorizing the results from the Major Research Plan into eight distinct categories, encompassing both data and non-data types. The data categories include emission inventory, chemical reanalysis, field observation, satellite observation, laboratory measurement, and source profile, comprising a total of 258 datasets. Non-data type results are further divided into new technology and online source apportionment technology, totaling 15 reports.

Here's a brief overview of some key data categories available on CAPDC:

• Emissions Inventory: Providing nine datasets covering various anthropogenic and natural sources , including a 10-km resolution emission inventory for China in 2017.
• Chemical Reanalysis: Comprising three datasets, including high-resolution air quality reanalysis and PM 2.5 composition data, continuously updated on the platform.
• Field Observation: Offering 221 datasets from 2011 to 2021, capturing field measurements in 41 cities, focusing on parameters such as cloud characteristics and aerosol parameters.
• Satellite Observation: Collating high-resolution data for various atmospheric pollutants through the Major Research Plan and the ChinaHighAirPollutants (CHAP) dataset.
• Laboratory Measurement: Encompassing physicochemical property parameters and chemical reaction parameters across six datasets.

The CAPDC website provides bilingual access in Chinese and English, facilitating functions such as project information inquiries, data retrieval, and downloading after registration and agreement to the data use terms. Notably, emissions inventory, chemical reanalysis, and satellite observation data can be previewed prior to downloading.

"Looking ahead, CAPDC aims to expand its repository with additional data and resources, continually enhancing user experience and bolstering efforts in combating air pollution." Said the PI of CAPDC, Prof. Mei Zheng from Peking University.

Journal information: Advances in Atmospheric Sciences

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China misses air quality goals as economy takes priority, report says

By Colleen Howe

BEIJING (Reuters) - Half of the Chinese cities targeted by the government for air quality improvements have missed their targets as the country prioritised strengthening the economy over cutting pollutants, research by a non-profit research organisation found.

China typically releases a winter air quality plan every autumn, because coal heating and atmospheric conditions lead to dirtier air during the winter months.

But the plan was not enforced in 2022, according to the Finnish-based Centre for Research on Energy and Clean Air (CREA) and was only reintroduced part-way through the 2023-2024 winter season.

The environment ministry could not immediately be reached for comment.

In Q4 2023, half of the cities targeted by the December air quality action plan missed their targets to cut concentrations of hazardous particles, known as PM2.5, while in Q1 2024 41% of the cities overshot the limits.

In the 2022-2023 winter season, PM2.5 levels jumped 4.7% year-on-year and only fell back 1.6% in the 2023-2024 winter, the report said.

The December 2023 plan by China's State Council, or cabinet, focused on reducing coal consumption in the Beijing-Tianjin-Hebei region, the Yangtze River Delta and northern China's Shanxi and Shaanxi.

Decreases in cement and coal production lowered emissions in Q1 2024, CREA said, but increases in coal-fired power, non-ferrous metals and petrochemicals offset part of that benefit. Industrial emissions make up around 62% of particulate emissions, the report said, according to 2022 data.

Overall, the report found that weather changes contributed more to pollution improvements than emissions changes. Pollution levels are affected by atmospheric conditions including rainfall, air temperature and pressure, and wind.

Even if met, China's goals are below air quality targets recommended by the World Health Organization, but CREA has previously said they would still be enough to prevent as many as 180,000 pollution-related deaths by 2025.

(Reporting by Colleen Howe; editing by Barbara Lewis)

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Original research article, controlling factors of high-quality reservoirs in low permeability sandstone: a case study of the upper member of the lower ganchaigou formation, qaidam basin.

• 1 Research Institute of Petroleum Exploration and Development, PetroChina, Beijing, China
• 2 China National Oil and Gas Exploration and Development Company Ltd., Beijing, China
• 3 China University of Geosciences, Beijing, China
• 4 Research Institute of Exploration and Development of Qinghai Oilfield Company, PetroChina, Dunhuang, China

The upper member of the Lower Ganchaigou Formation (UMoLGF) is a high-potential hydrocarbon exploration area in the North margin of the Qaidam Basin (NMoQB). It represents a typical low-permeability sandstone reservoir. The current understanding of reservoir characteristics of the UMoLGF is poor, and the main controlling factors of high-quality reservoir development remaining unclear. This study, for the first time, integrated various factors to investigate the formation mechanism of high-quality reservoirs in the UMoLGF’s low-permeability sandstone reservoirs. Results show three provenance systems developed in the study area: northwest, northeast, and east. The northwestern and northeastern areas share similar reservoir characteristics. The rock type is predominantly feldspar, with relatively poor particle sorting and rounding. Pore types are dominated by secondary dissolution pores. However, the northwestern area has more developed fractures and poorer pore structures than the northeastern. Meanwhile, in the eastern area, the rock fragment content was high, the rock type was mainly litharentie and lithic arkose, particles were well-sorted and well-rounded. Residual intergranular pores, with good structures, dominated the pore type. The UMoLGF has entered the eo-diagenesis B stage with minor progression into the meso-diagenesis A stage. Based on quantitive calculations, this study established porosity evolution models for the different study areas. The initial porosities in the northwestern, northeastern, and eastern areas were 30.8%, 30.4%, and 34.8%, respectively. Compaction and cementation are the major factors contributing to porosity reduction in the three areas, with the most significant impact in the northwestern area. Dissolution significantly improved the reservoir properties in the northwestern area, with little effect on the northeastern and eastern areas. The formation of high-quality reservoir in the UMoLGF was affected by provenance, diagenesis, and fractures, with the primary controlling factors varying by area. In the northwestern area, the formation of high-quality reservoirs benefited from strong dissolution and well-developed fractures. In the northeastern area, the high-quality reservoir was relied upon favorable provenance and dissolution. In the eastern area, provenance provided an excellent material basis for developing high-quality reservoirs, with dissolution and chlorite cementation further improving reservoir properties. This study provides a theoretical foundation for further exploration and development of UMoLGF and offers insights for exploring and developing similar low-permeability sandstone reservoirs.

1 Introduction

With the increasing global energy demand, low-permeability reservoirs have received considerable attention in recent years owing to their large oil and gas resource potential ( Hu, 2009 ; Liu et al., 2016 ; Hu et al., 2018 ; Wang et al., 2018 ; Li H. et al., 2019 ). Approximately 38% of the world’s hydrocarbon resources originates from low-permeability reservoirs ( Hu et al., 2018 ; Xie et al., 2022 ); accordingly, the low-permeability oil and gas fields are becoming increasingly important for global hydrocarbon development ( Cao et al., 2018 ; Hu et al., 2018 ; Xie et al., 2023 ). However, owing to varying policies, resources, and technological levels among countries, a uniform definition for low-permeability reservoirs has not been established ( Chen et al., 2019 ). In China, sandstone reservoirs with a permeability of ≤50 mD are defined as low-permeability sandstone reservoirs ( Yang et al., 2007 ; Hu, 2009 ; Cao et al., 2018 ). These reservoirs are mainly distributed in the Songliao, Ordos, Qaidam, Junggar, Sichuan, Tarim and other basins ( Hu, 2009 ; Zou et al., 2015 ; Hu et al., 2018 ; Wang, et al., 2018 ). Among these basins, the low-permeability reserves in Songliao, Ordos, Qaidam, and Junggar basins account for ≥85% of the total reserves ( Hu et al., 2018 ).

Low-permeability sandstone reservoirs are characterized by strong heterogeneity, complex pore structures, small pore throats, poor connectivity, and complex seepage mechanisms ( Zou et al., 2015 ; Cao et al., 2018 ; Wang, et al., 2018 ; Xie et al., 2023 ). Reservoir quality varies significantly in horizontal and vertical dimensions. Low permeability is the bottleneck restricting the effective development of low-permeability sandstone reservoirs. Hence, identifying high-quality reservoirs against a background of low permeability remains a significant challenge warranting urgent attention. High-quality low-permeability sandstone reservoirs refer to those with relatively good porosity and permeability ( Wang et al., 2003 ; Zou et al., 2009 ). Low-permeability sandstone reservoirs typically develop high-quality reservoirs, making them ideal for exploration and development ( Zou et al., 2013 ; Gao et al., 2018 ; Wu et al., 2019 ). However, in-depth analyses are needed to clarify the characteristics and the formation mechanisms of low-permeability and high-quality reservoirs, and to predict the distribution of high-quality reservoirs ( Zou et al., 2013 ; Liu et al., 2016 ; Cao et al., 2018 ; Chen et al., 2019 ).

Previous studies have identified key factors regulating the development of high-quality reservoirs in low-permeability sandstone reservoirs, including the sedimentary environment, tectonic movement, diagenesis, and hydrocarbon filling ( Wang et al., 2003 ; Zou et al., 2009 ; Yang et al., 2014 ; Liu et al., 2016 ; Zhou et al., 2017 ; Cui et al., 2019 ; Bai et al., 2021 ; Xie et al., 2023 ). Regarding the sedimentary environment, sand bodies formed in high-energy sedimentary environments tend to possess significant sedimentary thickness, low matrix content, high compositional maturity, coarse grain size, good sorting, high structural maturity, and high initial porosity ( Zheng et al., 2007 ; Bjørlykke, 2014 ; Zhou et al., 2017 ). These sand bodies exhibit an anti-compaction capacity and can maintain well-connected primary pores before undergoing dissolution transformation. The dominant sedimentary environment is often a favorable zone for developing high-quality reservoirs ( Zhou et al., 2017 ; Cao et al., 2018 ; Li S. S. et al., 2019 ). Meanwhile, dissolution is the primary diagenetic process that contributes to high-quality reservoirs. Based on different dissolution mechanisms, such as thermal evolution of organic matter, carbonate-clay reaction, atmospheric freshwater leaching, and clay mineral transformation ( Bjørlykke, 1993 ; Huang et al., 2003 ; Guo et al., 2009 ; Taylor et al., 2010 ; Zhao et al., 2015 ). For low-permeability sandstone reservoirs, the secondary pores formed by dissolution are essential controlling factors for developing high-quality reservoirs. Tectonic movements can create fractures, serving as channels for fluid flow and reservoir space for oil and gas. Faults and unconformities caused by these movements can also act as channels for organic acids, atmospheric fresh water, and deep hydrothermal fluids ( Shi et al., 2003 ; Jiang et al., 2015 ; Zhu et al., 2018 ). These channels aid the dissolution of nearby sandstone and the formation of secondary pores. Hydrocarbon filling can alter the pore fluid composition, inhibiting diagenesis to varying degrees ( Masst et al., 2011 ; Ji et al., 2015 ). Early hydrocarbon filling can effectively slow the reduction of porosity caused by compaction and cementation, which is conducive to forming high-quality reservoirs ( Bjørkum et al., 1993 ; Worden et al., 2018 ).

The upper member of the Lower Ganchaigou Formation (UMoLGF) is the key oil and gas exploration strata in the northern margin of the Qaidam Basin (NMoQB). It is characterized by various sedimentary facies, multi-provenance systems, complex diagenesis, and frequent tectonic movement. Although the UMoLGF represents a typical low-permeability sandstone reservoir, some areas have developed high-quality reservoirs with excellent porosity and permeability. Predecessors have conducted a lot of research on the factors affecting the development of the high-quality reservoirs in the UMoLGF. For example, Li et al. (2009) posited that rock composition and diagenesis significantly affect reservoir quality in the NMoQB. Sun et al. (2012) reported that diagenesis is the primary factor impacting the reservoir quality. In contrast, Chen et al. (2013) and Jia et al. (2014) proposed that sedimentary facies determine the reservoir quality in the NMoQB. However, these studies typically focused on single factors, resulting in one-sided conclusions. Overall, the current understanding of the reservoir characteristics of the UMoLGF remains relatively inadequate, and the primary factors impacting high-quality reservoir development in different areas are unclear. Thus, hydrocarbon exploration of the low-permeability sandstone reservoirs in this area is hindered.

Based on various analytical test data, this study is the first to integrated multiple factors (such as deposition, diagenesis, and tectonic processes) to explore the main controlling factors of high-quality reservoir development of low-permeability sandstone reservoirs within the UMoLGF. As such, the primary objectives of this study are to (a) analyze the provenance systems of the UMoLGF within the study area; (b) compare the reservoir characteristics in different areas; (c) clarify the diagenetic characteristics and establish porosity evolution models for the different areas of the UMoLGF; and (d) clarify the primary factors impacting the high-quality reservoir formation in different areas of the UMoLGF.

Collectively, this study advances the current understanding regarding the formation mechanism of the high-quality reservoirs in the UMoLGF’s low-permeability sandstone reservoirs and provides a theoretical foundation for further exploration and development within the area. Moreover, these insights can be applied to the exploration and development of similar low-permeability sandstone reservoirs.

2 Geological setting

Located in northwestern China, the Qaidam Basin is a large petroliferous basin that developed after the Indosinian movement ( Sun et al., 2010 ; Pang et al., 2022 ). The basin’s internal structure and sedimentary evolution were shaped by various tectonic movements, including the strike-slip movement of the Altyn Tagh Fault’s, northward subduction of the Indian Plate, and the uplift of the Qinghai–Tibet Plateau. The basin is irregularly diamond-shaped, featuring a high elevation in the northwest and a low elevation in the southeast ( Figure 1 ) ( Li et al., 2022 ; Wang et al., 2023 ).

Figure 1 . Map describing the structural units of the Qaidam Basin and marking location of the study area.

The Qaidam Basin has undergone various tectonic movements over time, including those of Caledonian, Hercynian, Indosinian, Yanshan, and Himalayan orogenies ( Feng et al., 2022 ). The basin consists of three structural units ( Figure 1 ) ( Chen et al., 2008 ; Fu, 2014 ). The NMoQB is an essential area for hydrocarbon exploration with well-structured oil storage and a widely and continuously distributed caprock. In the 1950s, the Lenghu Oilfield was discovered in the NMoQB. More, recently, oil and gas fields have been discovered in succession, including Mabei, Nanbaxian, Niudong, and Pingtai, indicating that the NMoQB has a high exploration potential ( Fu, 2014 ; Tian et al., 2020 ; Li et al., 2023 ). However, the overall exploration rate in this area is relatively low.

The Qaidam Basin has experienced substantial changes in its provenance, sedimentary center, and accumulation rate during the Cenozoic ( Wang et al., 2023 ). Compared with other regions, the tectonic movements of the NMoQB in the Cenozoic was particularly complex and changeable. From the Paleogene to the early Pliocene, the NMoQB remained stable and located on the edge of the Qaidam Basin for a long time. During the middle to late Pliocene-Quaternary, the NMoQB underwent significant tectonic deformation due to the strengthening of the tectonic movement in the late Himalayan period. This deformation included intra-basin strike-slip, basin margin thrust, and old mountain uplift, eventually forming the current structural features of the NMoQB ( Feng et al., 2022 ). Because of these complex tectonic movements, the NMoQB featured multi-provenance systems, substantial changes in the sedimentary environment, and complex factors controlling reservoir quality in the Cenozoic era ( Ma et al., 2016 ; Sun et al., 2019 ).

Based on outcrop, stratigraphic correlation, fossil, drilling, and logging analyse, the Cenozoic formations in the Qaidam Basin have been classified into seven formations. From old to new are as follows: the Lulehe Formation (E 1+2 ), the Ganchaigou Formation including the Lower Member of Lower Ganchaigou Formation (E 3 1 ) and the UMoLGF (E 3 2 ), the Upper Ganchaigou Formation (N 1 ), the Youshashan Formation including the Lower Youshashan Formation (N 2 1 ) and the Upper Youshashan Formation (N 2 2 ), the Shizigou Formation (N 2 3 ), the Qigequan Formation (Q 1+2 ), and the Dabuxunyanqiao Formation (Q 3+4 ) ( Figure 2 ) ( Guan and Jian, 2013 ; Jian et al., 2018 ; Jian et al., 2024 ).

Figure 2 . Mesozoic-Cenozoic stratigraphic column in the Qaidam Basin.

The study area within the NMoQB, with the Altun Mountains to the west and the Saishiteng, Xiaosaishiteng, and western Qilian Mountains to the north. It includes Niudong, Lenghu, Pingtai, Mabei, and Nanbaxian ( Figure 1 ). The target layer located in the UMoLGF ( Figure 2 ). The lower part of UMoLGF is mainly gray, yellow, and gray sandstone, with sandy mudstone and a small amount of argillaceous sandstone and siltstone. The upper part of UMoLGF is mainly gray, gray-green, brown, and gray sandstone, along with silt sandstone and sandy mudstone. As for the sedimentary environment, the Niudong, Lenghu, and platform areas developed fan delta–lacustrine systems, while the Mabei and Nanbaxian areas developed braided river delta–lacustrine systems.

3 Samples and methods

3.1 materials.

Samples from 25 wells were obtained from the UMoLGF. Heavy mineral, grain size, cast thin section (CTS), scanning electron microscopy (SEM), X-ray diffraction (XRD), high-pressure mercury injection, and physical analyses were conducted.

The Qinghai Oilfield Exploration and Development Research Institute shared data pertaining to the grain size for141 samples from 16 wells, the high-pressure mercury injection results for 69 samples from 15 wells, and the vitrinite reflectance (R 0 ) results for 15 samples from 5 wells.

3.2 Methods

To quantitatively analyze the clay mineral components and whole rock minerals of sandstones, 123 samples from 10 wells were selected for X-ray diffraction analysis (model: Empyrean Sharp Shadow). First, these samples were powdered using an agate mortar and a grinder, and then the clay fraction (<2 μm) was separated from the water based on the suspension method. The minerals composition can be deduced according to 550 °C heated and ethylene-glycol saturation diffractograms.

3.2.2 Heavy minerals

In total, 196 samples drilled from 25 wells were prepared for heavy mineral analysis. The samples were broken and fully dissolved with acid, followed by grinding and filtration. The sediment obtained after filtration was dried at 40 °C in an oven, then the fine sand composition was collected using a wet sieve. Subsequently, the dried samples were divided into heavy and light components with bromoform (2.89 g/cm 3 ). Various microscopes were used to identify light and heavy minerals, including binocular stereoscopes, polarizing microscopes, and microscopic analysis. This study utilized the strip number particle method to determine the percentage of light and heavy minerals present in each sample. The number of particles identified in each sample exceeded 300.

In total, 183 samples were collected from 15 wells and prepared for CTS observation. Blue-dyed epoxy resin was vacuum-impregnated into all the samples to visualize the pores. Alizarin red was used to stain the samples to identify carbonate minerals (red: calcite, purple: iron calcite, colorless: dolomite, and blue: iron dolomite). A polarizing microscope (Model: ZEISS imager. A2) was used to observe the thin sections. Point counting was used to determine debris composition, porosity, pore type, and structural characteristics, with 350 points per thin section.

3.2.4 Reservoir property

In total, 698 samples were collected from 15 wells to analyze the reservoir properties. They samples were made into cylinders with a 2.5 cm diameter. A porosity tester (Model: Ultra Pore-400) and a permeability tester (Model: DX-07G) were used to measure porosity and permeability, respectively.

SEM was utilized to identify the authigenic clay minerals and observe their pore structure and morphology. Gold-plated slices were prepared from 11 samples from five Wells and evaluated under an electron microscope (model: ZEISS EVO-18–18). Observed under high magnification, kaolinite, chlorite, and illite have distinct morphologies. Kaolinite typically presents a pseudo-hexahedron single mineral, and its aggregate is book-like or worm-like. Chlorite is needle-like or rose-like, and Illite is filamentous and flake. The illite-montmorillonite mixed layer is mainly in the forms of honeycomb and flake.

3.3 Quantitative analysis of porosity

Previous studies on pore evolution have mostly been qualitative analyses of diagenesis on reservoir control of the UMoLGF, lacking quantitative analysis. Based on CTS observation and reservoir properties analysis, this study quantitatively characterized the effect of different diagenesis on reservoir quality. The equations for quantitative calculation for porosity evolution are presented in Table 1 ( Beard and Weyl, 1973 ; Wang et al., 2017 ).

Table 1 . Equations for quantitative calculation of porosity evolution.

4.1 Provenance

The source rock type and transport distance of the provenance area directly determine the sediment composition. Heavy minerals are essential to sandstone debris, characterized by strong weathering resistance, wide distribution, and strong chemical stability ( Weltje and Von Eynatten, 2004 ; Xu et al., 2021 ). They can retain the parent rock’s characteristics more thoroughly when transporting and depositing with other sediments. Therefore, heavy minerals are an effective indicator of sediment provenance ( Weltje and Von Eynatten, 2004 ; Xu et al., 2021 ; Yi et al., 2023 ). Fourteen heavy mineral types were detected in the UMoLGF, among which magnetite predominated. Thus, magnetite was excluded from the analysis to avoid errors, and other minerals were quantified according to their relative percentages.

In the Niudong area, the heavy minerals included zircon (31.6%), tourmaline (31.5%), and epidote (12.5%). In the Lenghu and Pingtai areas, the heavy minerals included garnet (33.1%), zircon (24.0%), epidote (13.8%), and white titanium (15.3%). In the Mabei and Nanbaxian areas, the heavy minerals primarily included garnet (42.5%), white titanium (30.0%), and zircon (18.2%). Based on the heavy mineral analysis, three provenances of the UMoLGF were identified: northwest, northeast, and east ( Figure 3 ). The ultra-stable heavy mineral contents, such as zircon gradually decreased from west to east. Meanwhile, the contents of stable and moderately stable heavy minerals, such as garnet and epidote, gradually increased. Hence, the northwestern area was Figure 3 the closest to the provenance, followed by the northeastern area. The eastern area is the farthest from the provenance.

Figure 3 . Heavy mineral distribution of the UMoLGF in the NmoQB.

4.2 Petrological characteristics

The sandstone components in the three areas exhibited slight differences. In the northwestern area, the feldspar (avg. 37.02%) and rock fragment (avg. 37.98%) contents were relatively high, and the quartz content is lower (avg. 25.00%). In the northeastern area, the contents of feldspar (avg. 38.82%) and quartz (avg. 32.23%) were similar, whereas the rock fragment content was relatively low (avg. 28.95%). In the eastern area, the quartz content was relatively high (avg. 41.49%), and the feldspar (28.84%) and debris (29.67%) contents were low. The northwestern and northeastern areas share similar petrological characteristics - mainly arkose, lithic arkose, and feldspathic litharenite, with high feldspar content. In the eastern area, the rock types are primarily feldspathic litharenite and lithc arkose, with a relatively high rock fragment content ( Figure 4 ).

Figure 4 . The ternary diagram of the UMoLGF in different areas.

The particles in the northwestern and northeastern areas were mainly sub-angular. In contrast, the sandstone in the eastern area had a high level roundness, with sub-edges and sub-round shapes. Sorting in the northwest, northeast, and east was poor, medium–poor, and medium, respectively. The cementation throughout the study area was weak. The northwestern and northeastern areas were dominated by pore cementation, whereas the eastern area was dominated by pore and contact cementation.

4.3 Reservoir space

Reservoir space of the UMoLGF primarily included residual intergranular pores (RIPs), secondary dissolution pores, and fractures, with significant variation across the different areas ( Table 2 ; Figure 5 ).

Table 2 . The reservoir space types in different areas of the UMoLGF.

Figure 5 . Reservoir space of the UMoLGF: (A) N3, 912.6 m, intergranular and intragranular dissolution pores; (B) N5, 789.24 m, intragranular dissolution pores, fractures; (C) L95, 1106.74 m, intergranular and intragranular dissolution pores; (D) Y2, 1414.6 m, RIP, intergranular and intragranular dissolution pores; (E) MB15, 1356.78 m, RIP, intragranular pores; (F) XX1, 4210.23 m, RIP, intergranular and intragranular dissolution pores. RIP = residual intergranular pore.

RIPs were widely distributed throughout the three areas; however, they were more developed within the eastern area, accounting for 91.4% of the thin section porosity. The RIPs in the eastern area were diverse in shape, with triangles, polygons, and irregular shapes. The pores were large and well connected with flat edges ( Figures 5E, F ). The RIP contents in the northwestern and northeastern were lower, accounting for 16.12% and 39% of the thin section porosity, respectively ( Table 2 ), with fewer RIPs and with poor connectivity compared with those in the eastern area ( Table 2 ; Figures 5D–F ).

4.3.2 Secondary dissolution pores

This type of pore was critical in the UMoLGF and can be divided into intergranular and intragranular dissolution pores.

4.3.3 Intergranular dissolution pores

The intergranular dissolution pores were more developed in the northwestern and northeastern areas than in the eastern area ( Table 2 ; Figure 5 ). CTS observations showed that the intergranular dissolution pores have an uneven distribution and high level of connectivity. The edges of the dissolved particles were typically irregular, jagged, or uneven.

4.3.4 Intragranular dissolution pores

The development of intragranular dissolution pores varied among the different areas, with the highest level in the northwest, followed by the northeastern and the eastern areas ( Table 2 ). The CTS observations showed that these pores were mostly found in soluble particles, in the form of spots and honeycombs ( Figure 5 ). Carbonate cements, such as calcite cements, can be dissolved, forming some intragranular dissolution pores.

4.3.5 Fractures

Fractures occurred primarily in the northwestern area, characterized by a piedmont nose-like uplift. Multiple sets of fault systems developed under tectonic stress, resulting in the development of relatively well-developed structural fractures ( Figure 5B ). Fractures can serve as effective reservoir spaces and improve reservoir connectivity ( Figure 5B ). The fractures occasionally developed in the northeastern area but not the east.

Thin section porosities in the northwestern (5.15%) and northeastern (5.48%) areas were relatively similar ( Table 2 ). The secondary dissolution pores dominated the reservoir space in these two areas, with thin section porosity of 3.69% and 3.29%, respectively ( Table 2 ). However, fractures were more developed in the northwestern area, accounting for 12.23% of the thin section porosity ( Table 2 ).

The thin section porosity of the eastern area reached12.72%, with the pore types in dominated by RIPs (91.43%) ( Table 2 ), followed by secondary pores, (8.57%). Fractures were not developed ( Table 2 ).

4.4 Pore structure characteristics

High-pressure mercury injection is effective for studying pore-throat structures ( Zhang et al., 2017 ; Zhang et al., 2021 ). Based on reservoir classification standard of China (SY/T 6285–2011), the pore-throat structural of the UMoLGF was analyzed using displacement pressure (Pd) and the median pore throat radius (R 50 ) as the primary classification principle and combining the characteristics of the capillary pressure curve (CPC). The pore structure of the UMoLGF was classified into three types and six subtypes ( Table 3 ; Figure 6 ).

Table 3 . The Pore structure characteristics of UMoLGF in different areas.

Figure 6 . The capillary pressure curves in the (A) northwestern, (B) northeastern, and (C) eastern areas.

Type I1 is characterized by ultra-low displacement pressure (Pd < 0.05) and ultra-fine throat (R50 ≥ 25.0). The CPC was central and exhibited a coarse distortion. The reservoir corresponding to type I1 was the best of the UMoLGF. The pore types were dominated by RIPs, followed by secondary dissolution pores. The pores were well-developed with good connectivity.

Type I2 has ultra-low displacement pressure (0.05 ≤ Pd < 0.1) and an ultra-fine throat (15.0 ≤ R50 < 25.0). The CPC had a slight inclination toward the lower left with a rough skewness. The corresponding reservoir space primarily consisted of RIPs and intergranular dissolution pores with well-developed pores and pore connectivity.

Type II1 was featured with a low displacement pressure (0.1 ≤ Pd < 0.5) and an ultra-fine throat (5.0 ≤ R50 < 15.0) ( Table 3 ). The CPC exhibited a minor leftward and slightly rough skewness. The corresponding reservoir space primarily consisted of RIPs and intergranular dissolution pores with moderate pore structures.

Type II2 had the characteristics of medium displacement pressure (0.5 ≤ Pd < 2) and an ultra-fine throat (3.0 ≤ R50 < 5.0) ( Table 3 ). The CPC was central and slightly skewed. The corresponding reservoir space was mainly secondary dissolution pores with poor pore connectivity.

Type III1 was characterized by high displacement pressure (2.0 ≤ Pd < 5.0) and an ultra-fine throat (R50 < 3) ( Table 3 ). The CPC was slightly inclined toward the upper right with slight skewness. The corresponding reservoir developed a few RIPs with poor pore connectivity.

Type III2 was characterized by ultra-high displacement pressure (Pd ≥ 5) with no observable ( Table 3 ). The CPC was generally finely skewed to the upper right.

The mercury injection results revealed significant differences in pore structure among the three areas.

The northwestern area: Five types of pore-throat structures have developed. Type II2 was the most developed (48.39%), followed by type II1 (19.4%), type III1 (16.1%), and type III2 (12.9%). Type I2 was occasionally observed ( Table 3 ; Figure 6A ).

The northeastern area: Four types of pore throat structures developed: type II1 was the most common (37.5%), followed by type II2 (31.3%), type I2 (18.8%), and type III1 (12.5%) ( Table 3 ; Figure 6B ).

The eastern area: three types of pore structures have developed: type II1 was the most developed (40.9%), followed by Type I1 (31.8%) and Type II2 (27.3%) ( Table 3 ; Figure 6C ).

4.5 Porosity and permeability

Physical analysis results were analyzed according to the Chinese reservoir classification standard (SY/T6285-2011). Given that the permeability span of the UMoLGF is large, the median value was applied to evaluate the permeability. The results were as follows:

The northwestern area: porosity ranged from 3.4% to 28.1% (avg. 13.8%), mainly were low porosity ( Figure 7A ); Permeability varied from 0.04 to 85.8 mD (average: 4.75 mD, median: 1.0 mD), which were mainly ultra-low permeability ( Figure 7B ).

Figure 7 . Distribution histogram of porosity in the (A) northwestern, (C) northeastern, and (E) eastern areas. Permeability in the (B) northwestern, (D) northeastern, and (F) eastern areas.

The northeastern area: porosity varied between 2.0% and 27.1% (avg. 14.3%), dominated by low porosity ( Figure 7C ). The permeability span was relatively large, ranging from 0.02 to 89.5 mD (average: 4.97 mD, median: 0.57 mD) and dominated by ultra-low permeability ( Figure 7D ).

The eastern area: porosity ranged from 0.5% to 25.0% (avg. 8.7%), and was dominated by extra-low porosity ( Figure 7E ). The permeability span was large, varies from 0.02 to 826.9 mD (average: 74.7 mD, median: 0.85 mD) and dominated by ultra-low permeability ( Figure 7F ).

Overall, the UMoLGF is an ultra-low permeability reservoir, with varying porosity across different areas. The reservoir properties in the northwestern and northeastern areas were similar, While, the eastern area generally has relatively superior permeability.

4.6 Diagenesis

Combined the observation of CTS, SEM, and XRD, compaction, cementation, and dissolution were the main diagenetic events of the UMoLGF.

4.6.1 Compaction

The UMoLGF in the northwestern, northeastern, and eastern areas had medium–shallow burial. The following compactions can be observed under the microscope: (a) particles in the northwest, northeast, and east were mainly point contact ( Figures 8A,C,D ), accounting for 83.1%, 73.6%, and 69.0% ( Table 4 ), respectively, followed by floating contacts ( Figure 8B ), accounting for 10.8%, 14.1%, and 8.6% ( Table 4 ), respectively; (b) mica orientations were observed in the northwestern and northeastern areas ( Figures 8B,C ), and not in the eastern area ( Table 4 ); (c) a small number of point-line contact (5.2%), line contact (8.6%), and concave-convex contact (8.6%) were observed in the eastern area ( Table 4 ; Figures 8E,F ). Stylolite was present in the eastern area ( Figure 8F ) but absent in the northwestern and northeastern areas ( Table 4 ). Overall, the compaction intensity of the UMoLGF was weak.

Figure 8 . Compaction characteristics of the study areas in the UMoLGF: (A) N104, 1340.29 m, particles in point and line contact; (B) N5, 781.08 m, particles floating, directional arrangement of mica; (C) L87, 1411.35 m, particles in point contact, directional arrangement of mica; (D) L96, 804.07 m, particles in point contact; (E) MB15, 1456.78 m, particles in point contact; (F) XX1, 4213.42 m, particles in concave-convex contact, stylolite line.

Table 4 . Contact relationship between particles in different areas of the UMoLGF.

4.6.2 Cementation

The cementation types of UMoLGF include carbonate, clay mineral, and siliceous cementation.

4.6.3 Carbonate cementation

Calcite was the most critical carbonate cement in the UMoLGF and was present in more than 85% of the samples. The calcite content in the northwestern and northeastern areas was relatively high, with an average of 8.20% and 7.50%, respectively; however, it was relatively low in the eastern area (avg. 5.70%).

Two stages of calcite developed in the UMoLGF: (a) during the early stage of diagenesis, particles floated, and the compaction intensity was weak. Calcite cement precipitated at the edges of the particles or filled the intergranular pores as small particles. The content of this kind of calcite was low ( Figures 9A,B ). (b) in the meso-diagenesis stage, calcite crystals precipitated among the skeleton particles, and its content was slightly higher than the first stage of calcite ( Figure 9C ).

Figure 9 . Cementation characteristics of the UMoLGF: (A) N5, 780.9 m, the first stage of calcite; (B) L96, 993.8 m, the first stage of calcite; (C) L96, 1006.5 m, the second stage of calcite; (D) L95, 824.8 m, illite and I/S mixed layer filled the intergranular pores; (E) XX1, 4213.2 m, the quartz overgrowth, the I/S mixed layer; (F) XX1, 4111.75 m, chlorite grows perpendicular to the surface of particles in a needle-like shape; (G) XX1, 4115.3 m, quartz overgrowth and I/S mixed layer; (H) MX101, 834.6 m, smectite exists in a thin film; (I) MX101, 831.4 m, kaolinite in a page form.

4.6.4 Clay mineral cementation

Various clay minerals have been developed in the UMoLGF, including smectite, illite/smectite mixed layer (I/S mixed layer), chlorite, illite, and kaolinite. The clay mineral contents in different areas differed slightly. The I/S mixed layer (32.4% and 22.5%, respectively) and illite (30.1% and 31.9%, respectively) dominated the northwestern and northeastern areas, whereas illite (48.9%) and chlorite (27.8%) dominated the eastern area.

4.6.5 Smectite

The XRD results showed that smectite mainly existed in the northwestern, followed by the northeastern area, with minimal content in the east. According to the SEM observations, smectite typically wrapped mineral particles in a honeycomb-like and reticular aggregate, existing as a pore liner ( Figure 9H ).

4.6.6 I/S mixed layer

The I/S mixed layer mainly existed in honeycomb form ( Figures 9D, E, G ) and was widely developed in all three areas, with high content in the northwestern and northeastern areas.

4.6.7 Illite

Illite typically appeared as flakes and filaments. Illite aggregates were largely developed on particle as flakes ( Figure 9D ). Illite was the most important clay mineral in the three areas, with the highest content in the eastern area and similar contents in the northwestern and northeastern areas.

4.6.8 Chlorite

Chlorite was the most important clay mineral in the eastern area and less abundant in the northwestern and northeastern areas. Chlorite typically grew perpendicular to the particle surface in a needle-like shape and existed as a pore lining ( Figure 9F ).

4.6.9 Kaolinite

The kaolinite content was low in all three areas. Thin section observations showed that only a small number of the samples developed page-like kaolinite ( Figure 9I ).

4.6.10 Quartz overgrowth

Siliceous cementation in the UMoLGF was relatively limited, characterized by quartz overgrowth ( Figures 9E,G ). The siliceous cementation content in the three areas was in the order of eastern > northwestern > northeastern, the overall content was low.

4.6.11 Dissolution

Dissolution is a critical diagenetic event in the UMoLGF, that can significantly improve reservoir properties in the northwestern and northeastern areas. Under a microscope, various dissolution phenomena can be observed. (a) Acidic fluids dissolved the interior of particles, forming intragranular dissolution pores in a spotted honeycomb shape ( Figures 10A–F ); (b) acidic fluid dissolved the cleavage fracture of feldspar, forming micro-fractures to enhance pore connectivity ( Figure 10A ); (c) intergranular dissolution pores formed owing to irregular dissolution at the edges of soluble particles ( Figures 10A, B, D−F ); (d) dissolution occurred in calcite cement, forming intragranular dissolution pores ( Figures 10D,E ). These dissolution phenomena were more prevalent in the northwestern and northeastern areas but scarce in the eastern area.

Figure 10 . Dissolution characteristics of the study areas within the UMoLGF, (A) N3, 990.7 m, intergranular and intragranular dissolution pores, dissolution occurred along with feldspar cleavage fracture; (B) N104, 1636.48 m, the intergranular and intragranular dissolution pores; (C) Y2, 1422.5 m, dissolution occurred in the feldspar; (D) L96, 827.8 m, dissolution occurred in the feldspar, rock fragments, and calcite cement; (E) MB15, 1453.73 m, RIP and intergranular and intragranular dissolution pores; (F) XX1, 4208.16 m, intergranular and intragranular dissolution pores.

5 Discussion

5.1 diagenetic stage.

In this study, the diagenetic stages were divided based on the standard (SY/T5477-2003), and the basis for division was as follows.

a) Particles in the UMoLGF were mainly point contacts; b) Pores were mainly RIPs and secondary dissolution pores. The dissolution was dominated by feldspar dissolution, followed by rock fragment dissolution, and calcite dissolution; c) Ro is often used as a paleo-geothermal indicator of the organic matter and the basis for dividing the diagenesis stage. The Ro in the mudstone of the UMoLGF was between 0.47 and 0.53 (avg. 0.55); d) The distribution and morphology of authigenic minerals changed with formation temperature, pore fluid properties, and pressure. This formed different diagenetic minerals, corresponding to different diagenetic stages. The authigenic clay minerals of the UMoLGF were primarily illite, I/S mixed layer, and chlorite. The I/S mixed layer mostly developed on the grain surface in the form of a honeycomb; Illite predominately appeared as flakes. Chlorite grew on the surface as needle-like vertical particles and occasionally as rose-like particles. Smectite appeared as mesh and honeycomb-like particles. The kaolinite content was low, primarily appearing as book pages, and occasionally as worm-like particles. Two stages of calcite cementation were observed; quartz overgrowth was relatively rare and occasionally occurs in degrees of I-II.

Accordingly, the UMoLGF in the study area has entered the eo-diagenesis B stage, with minor progression into the meso-diagenesis A stage ( Figure 11 ).

Figure 11 . Porosity evolution models in different areas of the UMoLGF.

5.2 Porosity evolution model

Pore evolution is affected by sedimentation and diagenesis ( Liu et al., 2016 ; Zhou et al., 2017 ; Bai et al., 2021 ). Combined with the regional burial history, analytical test data, and porosity calculations ( Table 5 ), this study established pore evolution models for the three areas ( Figure 11 ).

Table 5 . Quantitative calculation of porosity evolution in different areas of the UMoLGF.

Eo-diagenetic stage A: The intensity of cementation and dissolution was weak at the early burial stage. Compaction was the main diagenesis event in this stage, leading to a reduction in porosity in the three areas. At this stage, the particles floated with no contact between thm, while the RIPs dominated the reservoir space. The northwestern and northeastern areas were near-source deposits. The sediment transport distance was relatively short, resulting in poor sorting and roundness. However, these variables were superior in the eastern area owing to its far distance. Controlled by the influence of provenance, the initial porosities in the northwestern and northeastern areas were similarly (30.8% and 30.4%, respectively), while the eastern area had a high initial porosity (34.8%) ( Table 5 ). The compaction intensity gradually increased with increased depth. At the end of this stage, particles were mainly in point contacts with few point-line contacts. Authigenic clay minerals, such as smectite and chlorite film, gradually precipitated, and calcite cement was observed ( Figure 11 ).

Eo-diagenetic stage B: After entering this stage, the compaction intensity was further enhanced, ultimately peaking. Some mica was oriented in the northwestern and northeastern areas but not in the east. Quantitative calculations showed that the pore loss rates caused by compaction in the northwestern, northeastern, and eastern areas were 73.2%, 50.7%, and 34.3%, respectively ( Table 5 ). The effect of dissolution on reservoir properties was gradually enhanced at this stage. Owing to the acidic fluid, the soluble substances were slightly corroded, forming secondary dissolution pores. Compared to the eastern area, the dissolution in the northwestern and northeastern areas was relatively strong. The influence of cementation on porosity was also enhanced at this stage. Crystalline calcite precipitated and filled the intergranular pores, and some kaolinite precipitated. Additionally, kaolinization of feldspar was observed in thin sections. Many illite and I/S mixed layers precipitated later in this stage. Cementation was relatively strong in the northwestern and northeastern areas. The dominant clay minerals in the northwestern and northeastern areas were I/S mixed layer and illite, while the eastern area was dominated by chlorite, illite, and I/S mixed layer ( Figure 11 ). During this stage, cementation had the most significant influence on the reservoir. The porosity loss rate caused by cementation was relatively close in the northwestern (20.04%) and northeastern areas (24.82%), which was relatively low in the eastern area (16.19%) ( Table 5 ).

Meso-diagenetic stage A: During this stage, the reservoir quality of the three areas was dominated by dissolution, with compaction and cementation having less impact. Chemical pressure dissolution occurred in the eastern area, and stylolite structure can be occasionally observed. This phenomenon was not detected in the northwestern or northeastern areas ( Figure 11 ). Meanwhile, organic acids were continuously released during the thermal maturation of organic matter, and soluble substances dissolved towing to acidic fluids, forming secondary dissolution pores. Dissolution significantly improved reservoir properties in the northwestern and northeastern areas with weak effects in the eastern area. After dissolution, the porosities in the northwestern, northeastern, and eastern increased by 35.40%, 24.17%, and 3.43%, respectively ( Table 5 ). Fractures were present in the northwestern and northeastern areas and not in the east ( Figure 11 ).

5.3 Controlling factors of reservoir quality

5.3.1 initial material, 5.3.1.1 grain size and sorting.

Different provenance systems determined the differences in the initial material composition of the three areas. Grain size and sorting were foundational factors in determining the reservoir quality.

Owing to the short sediment transport distance, the northwestern and northeastern areas exhibited poor sorting and a relatively large median grain sizes. The sediment transport distance was long as the eastern area was located futher from the provenance. The water system in the eastern area was one of the most significant in the Paleogene of the Qaidam Basin, providing abundant energy. Hence, the sandstone in the eastern area was relatively well sorted, with a relatively small median grain size.

The median grain size showed a weak positive correlation with porosity (R 2 = 0.14, 0.13, 0.05, respectively) ( Figure 12A ) and permeability (R 2 = 0.33, 0.31, 0, respectively) ( Figure 12B ) in the northwestern, northeastern, and eastern areas. The reservoir properties of the three areas were weakly influenced by grain size. The sorting in the eastern area exhibited a significant negative correlation between porosity (R 2 = 0.67) ( Figure 13A ) and permeability (R 2 = 0.53) ( Figure 13B ). Meanwhile, the sorting in the northwestern and northeastern areas showed a weak correlation with porosity (R 2 = 0.01 and 0.09) ( Figure 13A ) and permeability (R 2 = 0.03 and 0.14) ( Figure 13B ). Overall, the initial material significantly influenced reservoir quality in the eastern area compared to the northwestern and northeastern areas.

Figure 12 . Relationship between the median grain size and (A) porosity and (B) permeability in different areas.

Figure 13 . The cross-plots between the sorting and (A) porosity and (B) permeability in different areas.

5.3.1.2 Mud content

The mud content in the eastern area was substantially lower than that in the northwestern and northeastern areas. Given the hydrodynamic conditions in the eastern area, particles were fully elutriated during transportation, helping to preserve primary pores.

Mud content can greatly affect the reservoir properties. Under the overlying formation pressure, mud can directly fill the intergranular pores, reducing reservoir space and pore connectivity. Compared to the other areas, the northwestern area was significantly affected by mud content, with the effect on permeability (R 2 = 0.39) greater than on porosity (R 2 = 0.11) ( Figure 14 ). The mud content had minimal impact on the reservoir quality in the northeastern area and relatively no impact on the eastern area ( Figure 14 ).

Figure 14 . The cross-plots of the mud content and (A) porosity and (B) permeability in different areas.

5.3.2 Diagenesis

Diagenesis significantly affects sandstone reservoirs quality, which can determine the preservation of primary and formation of secondary pores ( Dou et al., 2023 ; Zhang et al., 2023 ). Owing to the differences in initial material composition, diagenesis can have varying impacts on reservoir quality.

5.3.2.1 Compaction

The UMoLGF burial depth was shallow, and the compaction was weak, significantly impacting the reservoir properties in all three areas. According to Houseknecht’s (1989) relationship between intergranular pore volume and cement content, compaction was the primary cause of reservoir deterioration in all three areas. Compaction had the most significant impact on the northwestern area, followed by the northeastern area and eastern areas ( Figure 15 ). The porosity loss rates owing to compaction significantly differed in the northwestern (73.19%), northeastern (50.66%), and eastern (34.30%) areas ( Table 5 ).

Figure 15 . Influence of compaction and cementation on porosity of the UMoLGF in different areas.

5.3.2.2 Cementation

1) Carbonate cementation

Carbonate cementation can positively and negatively impact reservoir properties ( Mansurbeg et al., 2008 ; Bjorlykke and Jahren, 2012 ; Zhang et al., 2015 ; Lai et al., 2018 ; Chen et al., 2019 ). It can directly fill the pores, reducing the reservoir properties. Alternatively, carbonate cement formed early can improve compaction resistance and protect primary pores. When subjected to acidic fluids, some carbonate cement dissolves and creates secondary pores, improving reservoir properties.

Carbonate cement was dominated by calcite cement in the UMoLGF. The calcite contents in the northwestern and northeastern areas were high (average: 7.60% and 8.10%, respectively), Whereas that in the eastern area was relatively low (avg. 5.70%). The influence of calcite content on reservoir properties differed among the three areas.

The northwest area: The relationship between calcite content and reservoir properties is complex. When the calcite content was ≤10%, the porosity and permeability decreased as it increased ( Supplementary Figure S1A, B ). However, when the calcite content was between 10% and 20%, the porosity and permeability increased as it increased ( Supplementary Figure S1A, B ). Conversely, if the calcite content exceeded 20%, both porosity and permeability decreased as it increased ( Supplementary Figure S1A, B ). This can be explained as follows.

In the early stage of diagenesis, the calcite content was relatively low, mainly filling the primary pores and occupying some reservoir space. At this stage, compaction dominated diagenesis, while cementation and dissolution were relatively weak. The calcite content increased as cementation progressed, and the damage to reservoir properties gradually increased. The calcite content showed a negative correlation with reservoir properties in the early stages. Over time, dissolution gradually increased with diagenesis and the continuous release of organic acids, primarily from the maturation of organic matter. During this period, some calcite particles dissolved, creating small secondary dissolution pores and improving reservoir properties. Consequently, the reservoir properties improved with increasing calcite content. However, when the it exceeded 20%, the pores created by calcite dissolution were insufficient to offset the pores occupied by calcite filling. Therefore, the reservoir properties deteriorated with increasing calcite content.

The northeastern area: Compared to the northwestern area, the dissolution in this area was relatively weak. When the calcite content was less than 18%, it negatively correlated with porosity (R 2 = 0.23) and permeability (R 2 = 0.24) ( Supplementary Figure S1C, D ). When the calcite content was greater than 18%, both porosity and permeability showed a slight increase with the increase of it ( Supplementary Figure S1C, D ).

The reasons for this were as follows: In the early stage, some calcite precipitated, and dissolution was weak. At this time, the calcite content was low and largely existed in the pores. As the calcite content increased, the reservoir properties gradually deteriorated. As diagenesis progressed, dissolution gradually increased, and calcite dissolved to form some secondary pores, improving the reservoir properties.

The eastern area: The calcite content was low, and negatively correlated with porosity (R 2 = 0.50) ( Supplementary Figure S1E ) and permeability (R 2 = 0.56) ( Supplementary Figure S1F ). Calcite cements mainly developed in intergranular pores and occupied a certain reservoir space, reducing reservoir properties. (2) Clay Mineral Cementation.

Clay mineral cementation is a crucial factor affecting reservoir properties. Reservoir properties were negatively correlated with clay mineral content in the study area. High clay mineral content was often associated with low porosity and permeability. Hence, the eastern area was significantly impacted by clay minerals compared to the northwestern and northeastern areas ( Supplementary Figure S2 ).

The clay mineral content varied across different areas. Based on XRD results, the primary clay minerals in the northwestern and northeastern areas were the I/S mixed layer and illite. In contrast, illite and chlorite dominated the clay minerals in the eastern area.

Northwestern and Northeastern areas: The I/S mixed layer (honeycomb shape) and illite (filamentous or curved sheet shape) tended to fill the intergranular pores and occupy some reservoir space. They can also segment the reservoir space, making the initially narrow throat more tortuous and reducing pore connectivity. The cross-plots of the I/S mixed layer, illite, and reservoir properties revealed a destructive effect on reservoir properties, with illite having a relatively larger influence ( Supplementary Figure S3 ). However, the overall impact was weak. The northeastern area was slightly affected by illite, and its effect on the porosity was more significant than on permeability ( Supplementary Figure S3 ).

Eastern area: Filamentous illite filled the intergranular pores in the eastern area with a well-developed crystal morphology. Illite negatively correlated with porosity (R 2 = 0.45) and permeability (R 2 = 0.49) ( Supplementary Figure S4 ), and exhibited a destructive effect on reservoir quality. Chlorite was well-developed in the eastern area, growing perpendicular to the particle surface in a pore-lining form, protecting the primary pores. The cross-plot of the chlorite content and reservoir properties showed that chlorite was positively correlated with porosity (R 2 = 0.26) and permeability (R 2 = 0.26) ( Supplementary Figure S4 ).

5.3.2.3 Dissolution

Dissolution plays a crucial role in improving the reservoir quality. Secondary dissolution pores were positively correlated with porosity (R 2 = 0.24, 0.57, and 0.11, respectively) ( Supplementary Figure S5A ), permeability (R 2 = 0.28, 0.57, and 0.05, respectively) ( Supplementary Figure S5B ), and thin section porosity (R 2 = 0.92, 0.45, and 0.71, respectively) ( Supplementary Figure S5C ) in the northwestern, northeastern. And eastern areas.

Quantitative calculation results reveal that dissolution affected the three areas differently. Dissolution in the northwestern area was the strongest, followed by the northeastern area and eastern areas. The porosity increase rates caused by dissolution were 35.40%, 24.17%, and 3.43%, respectively, for the northwestern, northeastern, and eastern areas. The main reasons are as follows. Dissolution is closely related to organic acids produced by source rocks, deep hydrothermal fluids, near-surface atmospheric freshwater leaching, and freshwater leaching under an unconformity surface. The Lower Jurassic reservoir, the source rock in the NMoQB, is widely developed in the northwestern and northeastern areas ( Yao et al., 2017 ). When organic matter is converted to hydrocarbons, it releases a significant amount of CO 2 and produces carboxylic acids. The Lower Jurassic reservoir in the NMoQB contains coal-bearing source rocks, resulting in a higher concentration of organic acids compared with other source rocks. Organic acids can then be transported to the UMoLGF through faults or pores, increasing the acidity of pore fluids in the northwestern and northeastern areas, and ultimately promoting dissolution.

The petrological characteristics showed that the feldspar content in the northwestern and northeastern areas was relatively high, and the rock fragments in these areas were dominated by volcanic and metamorphic rock debris, respectively. In contrast, in the eastern area, the feldspar content was low, the debris was mainly metamorphic rock debris, and the soluble volcanic rock debris was low. Under the action of acidic fluids, unstable minerals easily dissolve and form secondary pores. Overall, the northwestern and northeastern areas had a more suitable material basis for dissolution than the eastern area.

Furthermore, the conversion of clay materials produces acidic fluids, which promote dissolution. For example, H + is produced when smectite is converted into illite, increasing the acidity of diagenetic fluids. Indeed, the XRD results indicate that smectite content is higher in the northwestern and northeastern than the eastern. Thus, compared with the eastern area, the northwestern and northeastern areas had better conditions for dissolution.

5.3.3 Fractures

Fractures were primarily developed in the northwestern area, accounting for 12.23% of the total thin section porosity ( Table 2 ). The northwestern area features a sizable nose-shaped uplift with a piedmont tilt to the basin ( Wu et al., 2016 ; Ren et al., 2019 ). Owing to frequent tectonic activity, multiple sets of fault systems have developed in this area, resulting in various fractures. The presence of fractures facilitated the migration of acidic fluids, thereby promoting dissolution.

According to CTS observations, samples with fractures generally had higher permeability than those without fractures in the northwestern area ( Supplementary Figure S6A ). However, fractures had little influence on core porosity (R 2 = 0.05) ( Supplementary Figure S6B ) and thin section porosity (R 2 =0.07) (( Supplementary Figure S6D ), while positively correlating with core permeability (R 2 =0.46) (( Supplementary Figure S6C ). Thus, fractures are essential for improving the permeability in this area.

Oil and gas can effectively migrate through micro-fractures with openings larger than 0.1 µm ( Anders et al., 2014 ). Hence, given that the fracture opening in the northwestern area was 2.17–51.86 µm, the fractures could serve as a reservoir space and a dominant channel for oil and gas migration. This can significantly improve reservoir properties in the northwestern area.

5.4 Controlling factors of high-quality reservoir

According to the above analysis, reservoir quality of the UMoLGF is controlled by multiple factors such as provenance (e.g., sorting and mud content), diagenetic events, and fractures. However, high-quality reservoirs’ formation mechanisms and controlling factors significantly differ among the study areas.

The northwestern area: The reservoir space is well-developed, mainly with secondary dissolution pores, followed by fractures and a few RIPs. Fan delta deposits developed in this area. Soluble substance content (e.g., feldspar and debris) was high, conducive to dissolution. The organic acids produced in the Lower Jurassic source rocks further enhanced dissolution. Addtionally, this area has a large, nose-shaped uplift with a high northwest and a low southeast direction. The frequent tectonic activity in this area has led to multiple sets of fault systems. The associated fractures provide reservoir space and act as channels for hydrocarbon migration. Hence, in this area, the development of high-quality reservoirs relied on strong dissolution and widely developed fractures.

The northeast area: Pores were mainly secondary dissolution pores, followed by RIPs. This area also developed a fan delta sedimentary system. Compared to that in the northwestern area, reservoirs in this area have the following advantages: (a) the scale of sand deposits is more extensive and their distribution is wider; (b) sandstone particle sorting and pore structure are superior; (c) cementation is weaker; and (d) the soluble particle content is higher, and the Lower Jurassic source rocks providing organic acids. Thus, this area has favorable conditions for dissolution. In summary, the essential factors determining the high-quality reservoir include a favorable sedimentary environment that ensures the extensive distribution of sand bodies, and favorable dissolution leading to secondary dissolution pores.

The eastern area: RIPs and secondary dissolution pores dominated the reservoir space. The eastern area developed a braided river delta. Compared with those in the northwestern and northeastern areas, the eastern area has a better material basis: (a) the sorting of sandstone particles is good; (b) mud content is lower; (c) the sand body size is larger and its distribution is wider; and (d) pore structure is good. Regarding diagenetic transformation, compaction and cementation have little impact on reservoir properties in this area. Dissolution can create some secondary pores, and chlorite, which can protect RIPs, is widely developed in this area. Overall, the formation of high-quality reservoirs in this area depends on a favorable material basis, chlorite cementation, and dissolution.

6 Conclusion

1) Three provenance systems developed in the study area: northwest, northeast, and east. The northwestern and northeastern areas have similar reservoir characteristics with feldspar as the dominant rock type. Secondary dissolution pores dominate pore types. However, the northwestern area had more developed fractures and poorer pore structures compared to the northeastern area. In contrast, the eastern area contained a high rock fragment, with feldspathic litharenite and lithic arkose as the primary rock types. Furthermore, the sorting and roundness are superior and RIPs are the predominant pore type with a suitable pore structure.

2) UMoLGF has entered the eo-diagenesis B stage, with minor progression into the meso-diagenesis A stage. The pore evolution models in the northwestern and eastern areas were similar, whereas those in the eastern area slightly differed. The initial porosities in the northwestern, northeastern, and eastern areas were 30.8%, 30.4%, and 34.8%, respectively. Compaction significantly influenced the northwestern area. The porosity loss rates caused by compaction in the northwest, northeast, and eastern regions are 73.2%, 50.7%, and 34.3%, respectively. Cementation significantly affected the northwestern and northeastern areas, resulting in 20.0% and 24.8% porosity loss rates, respectively. In contrast, its impact on the eastern area was relatively small, with a porosity loss rate of 16.2%. Dissolution was essential for improving reservoir properties; the increased porosity rates owing to dissolution in the northwestern, northeastern, and eastern areas were 35.4%, 24.2%, and 3.4%, respectively.

3) The factors controlling high-quality reservoirs formation in the low-permeability UMoLGF differed among study areas. Compaction was the primary cause of porosity deterioration in all three areas. Strong dissolution and widely developed fractures significantly affected the northwestern high-quality reservoirs. Meanwhile, the northeastern area was dominated by provenance and diagenesis. The provenance provided a suitable material basis and favorable dissolution conditions can further improve reservoir quality. The eastern area was less affected by diagenesis; however, its provenance provided a good material basis. The sorting, rounding, pore type, and pore-throat structures were good. Dissolution and chlorite cementation can improve its reservoir properties.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author contributions

WL: Software, Writing–review and editing, Writing–original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal Analysis, Data curation, Conceptualization. DH: Writing–review and editing, Writing–original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal Analysis, Conceptualization. CG: Writing–review and editing, Supervision, Project administration, Methodology, Formal Analysis, Conceptualization. TF: Writing–review and editing, Supervision, Project administration, Formal Analysis, Conceptualization. YC: Writing–review and editing, Visualization, Investigation, Funding acquisition, Data curation. Ya’L: Software, Writing–review and editing, Validation, Resources, Investigation, Funding acquisition. QS: Software, Writing–review and editing, Validation, Resources, Investigation, Funding acquisition. QL: Writing–review and editing, Software, Investigation, Data curation.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was jointly funded by the project from the Research Institute of Exploration and Development of Qinghai Oilfield (Grant 2018-Technique-Exploration-02) and the PetroChina prospective basic strategic technology research project (Grant 2022DJ3209).

Acknowledgments

We sincerely thank the Research Institute of Exploration and Development of Qinghai Oilfield Company, PetroChina, for generously providing us with research data and samples. Additionally, we are grateful to the journal editor and reviewers for their valuable comments and suggestions that significantly improved the quality of this manuscript.

Conflict of interest

Authors WL, DH, YC, and QL were employed by PetroChina. Author CG was employed by China National Oil and Gas Exploration and Development Company Ltd. Authors Ya’L and QS were employed by Research Institute of Exploration and Development of Qinghai Oilfield Company, PetroChina.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest..

Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2024.1396061/full#supplementary-material

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Keywords: formation mechanism, high-quality reservoir, low-permeability sandstone reservoir, upper member of the lower Ganchaigou formation, Qaidam basin

Citation: Li W, Hu D, Gong C, Fan T, Chen Y, Li Y, Shi Q and Leng Q (2024) Controlling factors of high-quality reservoirs in low permeability sandstone: a case study of the upper member of the lower Ganchaigou formation, Qaidam basin. Front. Earth Sci. 12:1396061. doi: 10.3389/feart.2024.1396061

Received: 05 March 2024; Accepted: 09 April 2024; Published: 01 May 2024.

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Copyright © 2024 Li, Hu, Gong, Fan, Chen, Li, Shi and Leng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dandan Hu, [email protected]

This article is part of the Research Topic

Research Progress on the Formation Mechanism, Controlling Factors and Hydrocarbon Accumulation Patterns of Unconventional Oil and Gas Reservoirs Worldwide

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• Assessing quality of direct-to-consumer telemedicine in China: a cross-sectional study using unannounced standardised patients
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• Zhen Zeng 1 ,
• Dong (Roman) Xu 2 ,
• Yiyuan Cai 3 ,
• http://orcid.org/0000-0002-7943-4041 Wenjie Gong 1 , 4 , 5
• 1 HER Team and Department of Maternal and Child Health, Xiangya School of Public Health , Central South University , Changsha , China
• 2 SMU Institute for Global Health (SIGHT), School of Health Management and Dermatology Hospital , Southern Medical University (SMU) , Guangzhou , China
• 3 Department of Epidemiology and Health Statistics, School of Public Health , Guizhou Medical University , Guiyang , China
• 4 Institute of Applied Health Research , University of Birmingham , Birmingham B15 2TT , UK
• 5 Department of Psychiatry , University of Rochester , Rochester , New York , USA
• Correspondence to Professor Wenjie Gong, HER Team and Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410078, China; gongwenjie{at}csu.edu.cn

Direct-to-onsumer telemedicine (DTCT) has become popular as an alternative to traditional care. However, uncertainties about the potential risks associated with the lack of comprehensive quality evaluation could influence its long-term development. This study aimed to assess the quality of care provided by DTCT platforms in China using unannounced standardised patients (USP) between July 2021 and January 2022. The study assessed consultation services on both hospital and enterprise-sponsored platforms using the Institute of Medicine quality framework. It employed 10 USP cases, covering conditions such as diabetes, asthma, common cold, gastritis, angina, low back pain, child diarrhoea, child dermatitis, stress urinary incontinence and postpartum depression. Descriptive and regression analyses were employed to examine platform characteristics and compare quality across platform types. The results showed that of 170 USP visits across 107 different telemedicine platforms, enterprise-sponsored platforms achieved a 100% success in access, while hospital-sponsored platforms had a success rate of only 47.5% (56/118). Analysis highlighted a low overall correct diagnosis rate of 45% and inadequate adherence to clinical guidelines across all platforms. Notably, enterprise-sponsored platforms outperformed in accessibility, response time and case management compared with hospital-sponsored platforms. This study highlights the suboptimal quality of DTCT platforms in China, particularly for hospital-sponsored platforms. To further enhance DTCT services, future studies should compare DTCT and in-person care, aiming to identify gaps and potential risks associated with using DTCT as alternatives or supplements to traditional care. The potential of future development in enhancing DTCT services may involve exploring the integration of hospital resources with the technology and market capabilities of enterprise-sponsored platforms.

• Health services research
• Quality measurement
• Performance measures
• Patient-centred care

https://doi.org/10.1136/bmjqs-2024-017072

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Introduction

Direct-to-consumer telemedicine (DTCT) signifies a revolutionary global healthcare delivery model. It involves patients independently initiating medical services remotely, engaging directly with healthcare providers through text messaging or video/phone calls. By bypassing traditional intermediaries like referral clinicians or facilitators, DTCT empowers patients to access medical care swiftly and efficiently. 1 In China, DTCT is burgeoning, boasting over 1700 registered platforms, 2 each serving as an individual website or application for DTCT services. These platforms are categorised into two main types: hospital sponsored and enterprise sponsored. Hospital-sponsored platforms, associated with single physical hospitals, primarily use in-house medical staff and offer streamlined functions due to resource limitations. In contrast, enterprise-sponsored platforms, supported by larger corporations, provide access to a wider network of licensed physicians and offer a diverse range of functions with sophisticated user interfaces. 3 Despite enhanced accessibility and convenience compared with in-person care, DTCT faces quality challenges, such as communication difficulties and antibiotic misuse. 4 5 However, research on DTCT quality remains limited, especially in the context of China. To address this gap, our study employs unannounced standardised patients (USPs)—individuals trained and validated to portray specific medical conditions in a consistent and standardised manner 6 7 —to assess DTCT quality in China across different platform types based on the Institute of Medicine (IOM) quality framework.

This cross-sectional study examined both types of DTCT platforms that offered Chinese language services. The study was conducted between July 2021 and January 2022. Prior informed consent was waived due to minimal risk, and all analyses were performed on fully deidentified aggregated data. 8

To assess consultation service quality, we employed 10 different USP cases, each representing a specific medical condition (details in online supplemental eMethods and online supplemental example of a USP case ). Following thorough training and assessment, we selected 15 qualified USPs from the initial pool of 25 candidates. Each case was assigned at least one USP. As USPs’ initial requests for physician consultations could be denied for various reasons, mirroring real-world scenarios, each case made consultation requests until they completed at least five consultations on each platform type. The USPs captured screenshots and recorded the consultation process, including any failed attempts and reasons for failure. We evaluated the access success rate and assessed consultation quality using the IOM quality framework 9 ( table 1 ).

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Quality outcome indicators

We conducted descriptive and regression analyses to evaluate platform characteristics and quality outcomes. Regression analyses compared quality differences using ordinary least squares and logistic regressions, with hospital-sponsored platforms as the benchmark and controlling for patient case fixed effects. Adjusted differences and 95% CIs were reported, adjusted for platform-level clustering. Statistical significance was set at α=0.05. Stata SE (V.16.0) was used for all analyses.

Our study involved 170 visits, with 52 visits on 10 enterprise-sponsored platforms and 118 visits on 97 hospital-sponsored platforms (summarised in online supplemental eTable 1 ). The overall access rate was 63.5% (108/170). Enterprise-sponsored platforms achieved a 100% access rate (52/52), significantly higher than hospital-sponsored ones (56/118, 47.5%) (p<0.001). Common reasons for unsuccessful consultations included incomplete functions, like platforms claiming to offer DTCT services but lacking an accessible feature for initiating online consultations with physicians (25/62, 40.3%), and no response (13/62, 21.0%).

Of 108 successful consultations, 49 consultations (45%) received a correct diagnosis, while adherence to published guidelines was low for consultation (15%) and management decisions (31%). On average, physicians took 3 hours and 47 min to respond, with the total interaction time spanning 12 hours and 19 min. After controlling for disease case fixed effects and adjusting SEs for clustering at platform level, enterprise-sponsored platforms had higher rates of completed management decisions, shorter response times and higher costs ( table 2 ).

Comparison on main quality outcomes between platform types for successful consultations

In this study, we rigorously evaluated the quality of care across two types of DTCT platforms in China using USPs. With 108 successful visits out of 170 attempts, we found a diagnostic accuracy of approximately 45%, along with a decline in completion rates for recommended management decisions to 31%. This may be linked to the limited inquiries during USP encounters, with clinicians asking only about 15% of recommended consultation questions per visit. Besides traditional quality metrics, the timeliness of DTCT services is concerning, with a response time of 3 hours and 47 min and an overall interaction time of 12 hours and 19 min. In China, DTCT predominantly operates asynchronously, leading to these extended durations. Despite offering a more flexible alternative to in-person counselling by eliminating the need for travel, prolonged waiting times may still impact user satisfaction and the perceived effectiveness of DTCT platforms. Future research should prioritise enhancing the timeliness of DTCT services to ensure prompt access and timely interactions. The patient-centredness score averaging 2.4, indicating a medium to low level of patient-centred care, 10 is potentially influenced by less satisfactory outcomes discussed earlier in terms of effectiveness, safety and timeliness. These findings raise concerns about how effective DTCT services are. Further evaluation, including a direct comparison with in-person care, is needed for a clearer understanding of their quality. This can guide improvement measures, especially when DTCT services act as alternatives or supplements to traditional in-person care.

Notably, enterprise-sponsored platforms achieved 100% access success, surpassing hospital-sponsored ones at 47.5%. They exhibited superior performance in response times and completion rates for management decisions. Despite recent growth and policy support, 3 hospital-sponsored platforms seem to be in early developmental stages, potentially limiting medical resource accessibility. These findings challenge a marketing survey suggesting a preference for hospital-sponsored platforms, 11 emphasising higher access denial risks and less timely responses for consumers on these platforms.

This study was limited by the use of a uniform USP for each case. Using standardised scenarios with different USPs potentially allows for a comprehensive assessment of equity. However, due to constraints imposed by scripted scenarios in our study, this aspect was not explored.

Our study highlights the suboptimal quality of DTCT in China, specifically disparities between hospital-sponsored and enterprise-sponsored platforms. These findings likely echo broader challenges and principles inherent in DTCT globally. As DTCT gains momentum after COVID-19, future research becomes critical to effectively address these issues.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Xiangya School of Public Health (IRB No XYGW-2021-37). Prior informed consent was waived due to minimal risk and no individually identifiable information on physicians.

Acknowledgments

We sincerely thank Xiaohui Wang, Yaolong Chen, Yun Lu, Xiaojing Fan, Zhongliang Zhou, Jay Pan, and Chengxiang Tang for their unwavering leadership in development and management the SP cases. We also apperciate Lu Liu, Chunping Li, and Huanyu Hu for their their diligent efforts as project assistants, as well as all the standardized patients and study coordinators for their hard work.

• Elliott T ,
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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors WG conceived the study. ZZ coordinated the daily implementation of this study under the supervision of DRX, YC and WG. ZZ carried out data analysis and composed the initial manuscript draft, receiving guidance from WG and DRX. All authors contributed to critical review of the manuscript and approved the final draft.

Funding This study was funded by China Medical Board (20-368), Swiss Agency for Development and Cooperation (81067392) and the National Natural Science Foundation of China (82273643).

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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