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In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents. [ABSTRACT FROM AUTHOR]

Surface albedo plays a pivotal role in the Earth's energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen's slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. [ABSTRACT FROM AUTHOR]

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration. [ABSTRACT FROM AUTHOR]

The atmospheric dust caused by the cement industry is one of the main components of air pollutants. China is the largest producer and consumer of cement. It is challenging to balance cement needs and environmental protection. Based on the emission source data, this study examined the spatial and temporal patterns of PM2.5 by the cement industry's contribution (PM2.5Cement). The annual value of PM2.5Cement decreased from 1.40 × 106 µg/m3 in 2010 to 0.98 × 106 µg/m3 in 2017, which was reduced by 30.31%. I used the standard deviation ellipse and gravity center transfer method and identified that the cement industry center shifted from the east to the midwest of China, where a high-density population exists and a large portion of the population is exposed to the air pollution. The geographical detector method was used to analyze the contribution of the natural environment, green development, and socioeconomic development to PM2.5Cement. The main driving factors were identified as the socioeconomic development and the traffic conditions in 2010, which was giving way to the regional independent innovation in 2017. The cement industry's contributions to atmospheric PM2.5 vary spatially, suggesting that green development and optimized location for the cement industry are crucial to reducing the size of the population exposed to the pollutants. [ABSTRACT FROM AUTHOR]

China has experienced a series of COVID-19 recurrences in different cities across the country since 2020, and relatively strict (full lockdown) or lenient closure (semi-lockdown) strategies have been employed accordingly in each city. The differences in detailed transmission control measures during lockdown periods led to distinct effects on air quality, which has rarely been studied. To fill this gap, we examined the effects of semi-lockdown and full lockdown on six major airborne pollutants, based on 55 lockdown cases. For all lockdown cases, the concentrations of PM2.5, PM10, SO2, NO2 and CO were much lower than in previous years. Specifically, due to the stricter transmission control, the concentration of the five airborne pollutants experienced a much sharper decline during full lockdown. However, O3 presented a different variation pattern during lockdown periods. Generally, O3 concentrations presented a slight increase in semi-lockdown cases and a notable increase in full lockdown cases. Meanwhile, O3 increased notably in northern China, particularly in the Beijing–Tianjin–Hebei region, while O3 had a slight variation in southern China. The unique variation of O3 across regions and lockdown types was mainly attributed to the spatial heterogeneity of O3 formation regimes, especially the VOCs-controlled O3 formation in northern China. Based on Geographical Detector, we examined the spatial continuity of natural and socio-economic factors on the variation of airborne pollutants during lockdown. In terms of meteorological factors, humidity and precipitation were the dominant factors for PM2.5 and PM10, respectively, while humidity and temperature were the dominant factors for O3. In terms of socio-economic factors, the numbers of taxis and private cars were the dominant factors for PM2.5 and O3 variations during lockdown. GD also revealed that the combination of natural and socio-economic factors had a significantly enhanced effect on airborne pollutants during lockdown. The combination of relative humidity and total area of urban built-up areas exerted the strongest interactive effects on both PM2.5 and O3. This research highlighted the challenge for urban O3 management, and suggested the control of VOCs emissions should be preferably considered, especially in northern China. [ABSTRACT FROM AUTHOR]

CO2 emissions from fossil energy have caused global climate problems and threatened human survival. However, there are few studies on the spatiotemporal distribution and driving factors of carbon emissions. This paper takes the Yangtze River Delta (YRD) urban agglomeration as the research object and analyzes the spatiotemporal heterogeneity of carbon dioxide emissions and their driving factors from 2000 to 2017. First, a series of preprocessing, such as resample, interpolation, and image clipping, are conducted on the CO2 emission data and nighttime light remote sensing images. Second, the dynamic time wrapping (DTW) and hierarchical clustering algorithms were involved in manipulating the CO2 emission data. Consequently, the cities' and CO2 emissions' time series were classified into four categories and three stages separately. Finally, the geographical detector model (GDM) and geographical and temporal weighted regression (GTWR) are coupled to evaluate the spatiotemporal heterogeneity and quantify the driving factors. The results show the following: (1) The spatiotemporal distribution of CO2 emissions has spatial consistency from 2000 to 2017. High-emission areas are concentrated in economically developed areas such as Shanghai, Suzhou, and Wuxi. The results are consistent with previous research. (2) Regional aggregation is a revealed new trend. CO2 emissions in the target urban areas are gradually converging into economic center cities and diverse class cities, e.g., Shanghai and Ningbo. (3) In cities of different economic development levels, the driving factors of CO2 emissions are different. The secondary sector and urban infrastructure dominate in the early stages of developed cities. On top of that, the influence of the tertiary industry is more significant in the later development stages. According to the results, in the urban development process, humans should not only pursue the increase in speed but also pay attention to the negative impact of the economic development process on the ecological environment. Besides, since the spatiotemporal characteristics and dominant factors of urban carbon emissions are different in each stage of development, the formulation of carbon reduction policies should be associated with urban features. [ABSTRACT FROM AUTHOR]

Droughts have significantly damaged the environment of the Inner Mongolia Autonomous Region, China. In this study, the region was divided into two subregions. Soil moisture was used as the basic parameter to analyze the characteristics of agricultural droughts. Based on a geographical detector, the spatial stratified heterogeneity in different seasons was discussed. Moreover, the copula joint functions of characteristics and dominant factors of agricultural droughts were constructed. Based on the Soil Moisture Anomaly Percentage Index (SMAPI), the results demonstrate that the climate tendency rate of droughts in the summer and in spring in Subregion I shows an increasing trend, while it decreases in the autumn and winter. In Subregion II, the climate tendency rate of droughts in different seasons has no significant change. Through geographical detection, the single factor detection illustrates that temperature and Precipitation Conversion Efficiency (PCE) show the highest explanatory power in different subregions. The interactive detection also demonstrates the explanatory powers of the combination of the PCE and temperature, respectively. The t-copula function describes the correlation coefficients of the SMAPI with the PCE and temperature, with the optimal tail dependence. In short, agricultural droughts are most significantly affected by temperature and the PCE, and their balance has a significant impact on agricultural droughts.

Droughts have significantly damaged the environment of the Inner Mongolia Autonomous Region, China. In this study, the region was divided into two subregions. Soil moisture was used as the basic parameter to analyze the characteristics of agricultural droughts. Based on a geographical detector, the spatial stratified heterogeneity in different seasons was discussed. Moreover, the copula joint functions of characteristics and dominant factors of agricultural droughts were constructed. Based on the Soil Moisture Anomaly Percentage Index (SMAPI), the results demonstrate that the climate tendency rate of droughts in the summer and in spring in Subregion I shows an increasing trend, while it decreases in the autumn and winter. In Subregion II, the climate tendency rate of droughts in different seasons has no significant change. Through geographical detection, the single factor detection illustrates that temperature and Precipitation Conversion Efficiency (PCE) show the highest explanatory power in different subregions. The interactive detection also demonstrates the explanatory powers of the combination of the PCE and temperature, respectively. The t-copula function describes the correlation coefficients of the SMAPI with the PCE and temperature, with the optimal tail dependence. In short, agricultural droughts are most significantly affected by temperature and the PCE, and their balance has a significant impact on agricultural droughts. [ABSTRACT FROM AUTHOR]