A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM_2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM_2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM_2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM_2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM_2.5 changes. PM_2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM_2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM_2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m⁻³ m⁻¹ s. This study highlighted the impact of meteorological conditions on PM_2.5 growth, although it was much smaller than the anthropogenic emissions impact.

The visible infrared imaging radiometer suite (VIIRS) environmental data record aerosol product (VIIRS_EDR) and the aqua-moderate resolution imaging spectroradiometer (MYD04) collection 6 (C6) aerosol optical depth (AOD) products are validated against the Cimel sun–photometer (CE318) AOD measurements during different air quality conditions over the Yangtze river basin (YRB) from 2 May 2012 to 31 December 2016. For VIIRS_EDR, the AOD observations are obtained from the scientific data set (SDS) “aerosol optical depth at 550 nm” at 6 km resolution, and for aqua-MODIS, the AOD observations are obtained from the SDS “image optical depth land and ocean” at 3 km (DT3K) and 10 km (DT10K) resolutions, “deep blue aerosol optical depth 550 land” at 10 km resolution (DB10K), and “AOD 550 dark target deep blue combined” at 10 km resolution (DTB10K). Results show that the high-quality (QF = 3) DTB10K performs the best against the CE318 AOD observations, along with a higher R (0.85) and more retrievals within the expected error (EE) ± (0.05 + 15%) (55%). Besides, there is a 10% overestimation, but the positive bias does not exhibit obvious seasonal variations. Similarly, the DT3K and DT10K products overestimate AOD retrievals by 23% and 15%, respectively, all over the year, but the positive biases become larger in spring and summer. For the DB10K AOD retrievals, there is an overestimation (underestimation) in autumn and winter (spring and summer). Compared to the aqua-MODIS AOD products, the VIIRS_EDR AOD retrievals are less correlated (R = 0.73) and only 44% of the retrievals fall within EE. Meanwhile, the VIIRS_EDR shows larger bias than the aqua-MODIS C6 retrievals, and tends to overestimate AOD retrievals in summer and underestimate in winter. Additionally, there is an underestimation for the VIIRS_EDR AOD retrievals over the regions during high aerosol loadings. These indicate that the VIIRS_EDR retrieval algorithm needs to be improved in further applications over the YRB.

Exploring how urban form affects the Particulate Matter 2.5 (PM_2.5) concentration could help to find environmentally friendly urbanization. According to the definition of geography, this paper constructs a comprehensive urban form evaluation index system applicable to many aspects. Four urban form metrics, as well as road density and five control variables are selected. Based on 2015 data on China’s 340 prefecture-level cities, the spatial regression model and geographically weighted regression model were used to explore the relationship between the urban form evaluation index system and PM_2.5 pollution. The main results show that the spatial distribution of PM_2.5 in China follows an increasing trend from northwest to southeast. Urban form indicators such as AI, LPI, PLAND, LSI and road density were all significantly related to PM_2.5 concentrations. More compact urban construction, lower fragmentation of urban land, and lower density of the road network are conducive factors for improving air quality conditions. In addition, affected by seasonal changes, the correlation between urban form and PM_2.5 concentration in spring and winter is higher than that in summer and winter. This study confirmed that a reasonable urban planning strategies are very important for improving air quality.

The spatiotemporal variation of aerosol optical depth at 550 nm (AOD₅₅₀), Ångström exponent at 470–660 nm (AE_470–660), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth’s Radiant Energy System (CERES) are quantitatively analyzed over the Yangtze River Basin (YRB) in China during 2001–2015. The annual and seasonal frequency distributions of AE_470–660 and AOD₅₅₀ reveal the dominance of fine aerosol particles over YRB. The regional average AOD₅₅₀ is 0.49 ± 0.31, with high value in spring (0.58 ± 0.35) and low value in winter (0.42 ± 0.29). The higher AOD₅₅₀ (≥0.6) is observed in midstream and downstream regions of YRB and Sichuan Basin due to local anthropogenic emissions and long-distance transport of dust particles, while lower AOD₅₅₀ (≤0.3) is in high mountains of upstream regions. The IADRE is estimated using a linear relationship between SW upward flux and coincident AOD₅₅₀ from CERES and MODIS at the satellite passing time. The regional average IADRE is −35.60 ± 6.71 Wm⁻², with high value (−40.71 ± 6.86 Wm⁻²) in summer and low value (−29.19 ± 7.04 Wm⁻²) in winter, suggesting a significant cooling effect at TOA. The IADRE at TOA is lower over Yangtze River Delta (YRD) (≤−30 Wm⁻²) and higher in midstream region of YRB, Sichuan Basin and the source area of YRB (≥−45 Wm⁻²). The correlation coefficient between the 15-year monthly IADRE and AOD₅₅₀ values is 0.63, which confirms the consistent spatiotemporal variation patterns over most of the YRB. However, a good agreement between IADRE and AOD is not observed in YRD and the source area of YRB, which is probably due to the combined effects of aerosol and surface properties.

Urban forms, such as size, shape, density, compactness, and fragmentation, are associated with local air pollution concentrations. However, empirical analyses on how urban form improves or degrades urban air quality are still limited and inconclusive, especially for those rapidly expanding cities in developing countries. In this study, by using the improved STIRPAT (stochastic impacts by regression on population, affluence, and technology) model, the quantitative impact of urban form on near-surface PM_2.5 and NO₂ concentrations was identified in the 10 prefecture-level cities of the Yangtze River Basin (YRB) from 2000 to 2013. Trend analyses showed a significant increasing trend in both PM_2.5 (9.69 × 10⁻⁴ µg·m⁻³·year⁻¹) and NO₂ (1.73 × 10⁻⁴ ppb·year⁻¹) for the whole study period. Notably, a turning point of PM_2.5 from increasing to decreasing trends occurred around 2007. In addition, both pollutants showed a spatial agglomeration. The STIRPAT model demonstrated that socioeconomic, transportation and urban form factors played an important role in alleviating the increase of PM_2.5 and NO₂. In particular, a 1% decrease in urban extent density (UED) significantly increased NO₂ by 0.203%, but reduced PM_2.5 by 0.033%. The proximity index (PI) measured as a city’s compactness was significantly negatively correlated with PM_2.5 and NO₂. Conversely, a significant positive relationship of PM_2.5 and NO₂ concentrations against the openness index (OI) was observed, an important variable for measuring a city’s fragmentation. In addition, the environmental Kuznets curve (EKC) hypothesis between per capita GDP and PM_2.5 concentration was confirmed but failed in NO₂. Overall, this study encouraged a less fragmented and more compact urban form, which helped alleviate local air pollution concentrations by enhancing urban connectivity, reducing vehicle dependence, and facilitating the use of bicycles and walking.

This study intends to build a theoretical mechanism of agricultural socialization services for the eco-efficiency of agricultural land use from two paths, namely the agricultural division of labor and technological progress, and empirically calibrate the Tobit model based on the data of the China Family Tracking Survey (CFPS) for 4453 farming households in 23 provinces (autonomous regions and municipalities) across China. The results of the study show the following: (1) Agricultural socialization services can significantly improve the eco-efficiency of agricultural land use. (2) Hired labor services are more likely to promote eco-efficiency in agricultural land use than farm machinery leasing, especially in major food-producing areas; however, in non-major food-producing areas, the effectiveness of farm machinery leasing services is limited or affected by regional policy differences. (3) In the analysis of the mechanism of agricultural socialization services on the eco-efficiency of agricultural land use, the division of labor in agriculture plays an intermediary role, and the intermediary effect accounts for about 11.4%; however, there is a masking effect of technological progress. This means that China should further develop the role of agricultural socialized services in promoting the ecological efficiency of agricultural land use by developing agricultural socialized service organizations, accelerating the integration of farmers into the modern agricultural division of labor, and promoting the application of green agricultural machinery, among other measures.