The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data

To examine what built environment characteristics improve the health outcomes of human beings is always a hot issue. While a growing literature has analyzed the link between the built environment and health, few studies have investigated this relationship across different spatial scales. In this study, eighteen variables were selected from multi-source data and reduced to eight built environment attributes using principal component analysis. These attributes included socioeconomic deprivation, urban density, street walkability, land-use diversity, blue-green space, transportation convenience, ageing, and street insecurity. Multiscale geographically weighted regression was then employed to clarify how these attributes relate to cardiovascular disease at different scales. The results indicated that: (1) multiscale geographically weighted regression showed a better fit of the association between the built environment and cardiovascular diseases than other models (e.g., ordinary least squares and geographically weighted regression), and is thus an effective approach for multiscale analysis of the built environment and health associations; (2) built environment variables related to cardiovascular diseases can be divided into global variables with large scales (e.g., socioeconomic deprivation, street walkability, land-use diversity, blue-green space, transportation convenience, and ageing) and local variables with small scales (e.g., urban density and street insecurity); and (3) at specific spatial scales, global variables had trivial spatial variation across the area, while local variables showed significant gradients. These findings provide greater insight into the association between the built environment and lifestyle-related diseases in densely populated cities, emphasizing the significance of hierarchical and place-specific policy formation in health interventions.

Interaction of air pollution and meteorological factors on IVF outcomes: A multicenter study in China

BACKGROUND

Previous studies revealed associations between air-pollutant exposure and in vitro fertilization (IVF) outcomes. However, modification effects of air pollution on IVF outcomes by meteorological conditions remain elusive.

METHODS

This multicenter retrospective cohort study included 15,217 women from five northern Chinese cities during 2015-2020. Daily average concentrations of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO) and meteorological factors (temperature, relative humidity, wind speed, and sunshine duration) during different exposure windows were calculated as individual approximate exposure. Generalized estimating equations models and stratified analyses were conducted to assess the associations of air pollution and meteorological conditions with IVF outcomes and estimate potential interactions.

RESULTS

Positive associations of wind speed and sunshine duration with pregnancy outcomes were detected. In addition, we observed that embryo transfer in spring and summer had a higher likelihood to achieve a live birth compared with winter. Exposure to PM_2.5, SO₂, and O₃ was adversely correlated with pregnancy outcomes in fresh IVF cycles, and the associations were modified by air temperature, relative humidity, and wind speed. The inverse associations of PM_2.5 and SO₂ exposure with biochemical pregnancy were stronger at lower temperatures and humidity. Negative associations of PM_2.5 with clinical pregnancy were only significant at lower temperatures and wind speeds. Moreover, the effects of O₃ on live birth were enhanced by higher wind speed.

CONCLUSIONS

Our results suggested that the associations between air-pollutant exposure and IVF outcomes were modified by meteorological conditions, especially temperature and wind speed. Women undergoing IVF treatment should be advised to reduce outdoor time when the air quality was poor, particularly at lower temperatures.

Assessing the groundwater loss risk in Beijing based on ecosystem service supply and demand and the influencing factors

Incorporating ecosystem service supply and demand into ecological risk assessment can overcome the limitations of the traditional assessment framework. However, most previous studies are about theoretical discussions and applications of the assessment frameworks are very limited. In this study, we proposed an ecological risk assessment framework based on the supply and demand of ecosystem services and applied this framework to assess groundwater loss risk in Beijing. We calculated the water conservation service supply using the water balance equation and estimated the demand of the service using socioeconomic data from multiple sources. Moreover, the risk characterized by the risk probability of groundwater loss based on the budget of water conservation service was quantified. Furthermore, we delineated the spatial distribution characteristics of groundwater loss risk and analyzed natural and socio-economic factors affecting the risk using the geographically weighted regression (GWR). We found that the spatial distribution of water conservation supply and demand showed a mismatch. Moreover, high and very high groundwater loss risks were mainly distributed in the urban areas and on the cropland, and the very low risks were mainly located in the mountainous areas of Beijing. The average risk values in more than half of the administrative districts were >0.75 and parts of the new urban development areas displayed high groundwater loss risks. According to the GWR model, the impacts of the natural factors on the groundwater loss risk displayed larger spatial variations than those of the socioeconomic factors. Among the factors, population density exhibited a positive effect in most areas of Beijing and mainly affected the groundwater loss risk by influencing the water conservation service demand. Our study can provide a new perspective for ecological risk assessment in social-ecological systems and may provide scientific basis for the reduction of groundwater loss risk in Beijing.

How does COVID-19 lockdown affect air quality: Evidence from Lanzhou, a large city in Northwest China

Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.

A compensation mechanism for air pollutants generated by tourism-related land-based transportation: An exergy-based case study from Macao

This paper discusses the compensation standard for exhaust pollution and devises a compensation mechanism for Macao's tourism-related transport sector based on an integration of chemical exergy and universal exergy, using data on gasoline consumption by automobile sector retrieved from the transportation industry. The results reveal that: (1) the exergy values of air pollutant emissions increased from 1.53 × 10¹² kJ in 2010 to 2.03 × 10¹² kJ in 2019 (an increase of 1.33 times), and the exergy of CO, NO_x, and SO₂ emissions accounted for 77.5%, 20.4% and 2.1% of total exhaust emissions in Macao respectively. (2) In 2019, the monetary value of emission exergy, and the environmental costs of air pollution, were 1.7 times greater than in 2010. (3) If Light Rail Transit is compensated for, then the mean interval's values of the upper and lower limits of the compensation standard are 0.55 USD and 0.05 USD, respectively. When gasoline tax is used as a means of compensation it is necessary to raise its rate by about 8% based on the tax rate. A three-stage bargaining game model is used to provide evidence that this compensation standard is practical and acceptable.

Spatial characteristics of change trends of air pollutants in Chinese urban areas during 2016-2020: The impact of air pollution controls and the COVID-19 pandemic

Air pollution is a threat to public health in China, and several actions and plans have been implemented by Chinese authorities in recent years to mitigate it. This study examined the spatial distribution of changes in urban air pollutants (UAP) in 336 Chinese cities from 2016 to 2020 and their responses to air pollution controls and the COVID-19 pandemic. Based on the harmonic model, decreases in fine particles (PM_2.5), inhalable particles (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) levels were found in 90.7%, 91.9%, 75.2%, 94.3%, and 88.7% of cities, respectively, while an increase in ozone (O₃) was found in 87.2% of cities. Notable spatial heterogeneity was observed in the air pollution trends. The greatest improvement in air quality occurred mainly in areas with poor air quality, such as Hebei province and its surrounding cities. However, some areas (i.e., Yunnan and Hainan provinces) with good air quality showed a worsening trend. During the 13th Five-Year Plan period (2016-2020), the remarkable effects of PM_2.5 and SO₂ pollution control plans were confirmed. Additionally, economic growth in 74.2% of the Chinese provinces decoupled from air quality after implementing pollution control measures. In 2020, several Chinese cities were locked down to reduce the spread of COVID-19. Except for SO₂, the national air pollution in 2020 improved to a greater extent than that in 2016-2019; In particularly, the contribution of simulated COVID-19 pandemic to NO₂ reduction was 66.7%. Overall, air pollution control actions improved urban PM_2.5, PM₁₀, SO₂, and CO, whereas NO₂ was reduced primarily because of the COVID-19 pandemic.

Early life exposure to outdoor air pollution and indoor environmental factors on the development of childhood allergy from early symptoms to diseases

BACKGROUND

The prevalence of childhood allergies has increased during past decades leading to serious hospitalization and heavy burden worldwide, yet the key factors responsible for the onset of early symptoms and development of diagnosed diseases are unclear.

OBJECTIVE

To explore the role of early life exposure to ambient air pollution and indoor environmental factors on early allergic symptoms and doctor diagnosed allergic diseases.

METHODS

A retrospective cohort study of 2598 preschool children was conducted at 36 kindergartens in Changsha, China from September of 2011 to February of 2012. A questionnaire was developed to survey each child's early onset of allergic symptoms (wheeze and rhinitis-like symptoms) and doctor diagnosis of allergic diseases (asthma and rhinitis) as well as home environments. Each mother's and child's exposures to ambient air pollutants (PM₁₀, SO₂, and NO₂) and temperature were estimated for in utero and postnatal periods. The associations of early symptoms and diagnosed diseases with outdoor air pollution and indoor environmental variables were examined by logistic regression models.

RESULTS

Childhood early allergic symptoms (33.9%) including wheeze (14.7%) and rhinitis-like symptoms (25.4%) before 2 years old were not associated with outdoor air pollution exposure but was significantly associated with maternal exposure of window condensation at home in pregnancy with ORs (95% CI) of 1.33 (1.11-1.59), 1.30 (1.01-1.67) and 1.27 (1.04-1.55) respectively, and was associated with new furniture during first year after birth with OR (95% CI) of 1.43 (1.02-2.02) for early wheeze. Childhood diagnosed allergic diseases (28.4%) containing asthma (6.7%) and allergic rhinitis (AR) (7.2%) were significantly associated with both outdoor air pollutants (mainly for SO₂ and NO₂) during first 3 years and indoor new furniture, redecoration, and window condensation. We found that sex, age, parental atopy, maternal productive age, environmental tobacco smoke (ETS), antibiotics use, economic stress, early and late introduction of complementary foods, and outdoor air pollution modified the effects of home environmental exposure in early life on early allergic symptoms and diagnosed allergic diseases.

CONCLUSION

Our study indicates that early life exposure to indoor environmental factors plays a key role in early onset of allergic symptoms in children, and further exposure to ambient air pollution and indoor environmental factors contribute to the later development of asthma and allergic rhinitis.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective

Background

Fine particulate matter (PM_2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM_2.5 pollution is controversial in China.

Methods

In this study, we explored spatial-temporal characteristics and driving factors of PM_2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR).

Results

PM_2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m^-3 a^-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM_2.5 concentrations. The driving force of socioeconomic factors on PM_2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM_2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM_2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013.

Conclusion

We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM_2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM_2.5 emission and put forward effective policy to alleviate haze pollution.

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO₂), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO₂ concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO₂ concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO₂ concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO₂ concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO₂ concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes

La Oroya is a city in the Peruvian Andes that has suffered a serious deterioration in its air quality, especially due to the high rate of sulfur dioxide (SO2) emissions, which underlines the importance of knowing its sources of contamination and variation over the years. In this sense, this study aimed to evaluate the immission levels and determine the sources of SO2 contamination in La Oroya. This analysis was performed using the hourly concentration data of SO2, and meteorological variables (wind speed and direction), which were analyzed for a period of three years (2018-2020). Graphs of time series, wind and pollutant roses, bivariate polar graphs, clustering k-means, nonparametric statistical tests, and the application of the conditional bivariate probability function were performed to analyze the data and identify the emission sources. The mean concentration of SO2 was 264.2 μg m-3 for the study period, where 55.66 and 2.37% of the evaluated days exceeded the guideline values recommended by the World Health Organization and the Peruvian Environmental Quality Standard for air for 24 h, respectively. The results showed a defined pattern for the daily and monthly variations, with peaks in the morning hours (0900-1000 h LT) and at the end of the year (December), respectively. The main sources of SO2 emissions identified were light and heavy vehicles that travel through the Central Highway, the La Oroya Metallurgical Complex, the transit of vehicles within the city, and the diesel-electric locomotives that provide cargo transportation services and tourism passenger transportation. The article attempts to contribute to the development of adequate air quality management policies.

Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region

Identifying the effects of meteorological conditions on PM_2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM_2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM_2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM_2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM_2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM_2.5 pollution. Specifically, the effects of meteorological factors on PM_2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM_2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM_2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM_2.5 pollution and its links with meteorological conditions.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN

This paper proposed implementing a water and air monitoring system using sensor development and a LoRa Network. To transmit data, a self-made PCB board integrates the terminal sensors with Renesas RX64M MCU and LoRa. There are 16 monitoring point stations for the media experiment. The sensors were used to measure the water and air parameters such as PM2.5, CO2, DO concentration, pH level, temperature, and humidity. In addition, the Grafana system was implemented to present the status and variation in the monitoring parameters in the environmental area. To evaluate the monitoring system, we also collected public information provided by the environmental protection department of the Taiwan government at the same monitoring point for comparison.

Assessment of the association between prenatal exposure to multiple ambient pollutants and preterm birth: A prospective cohort study in Jinan, east China

Air pollution has been documented with a series of adverse pregnancy outcomes, yet their reproductive and developmental toxicity on human beings has not been fully elucidated. Here, we analyzed the geographic distribution of Jinan and examined its contribution to air pollution. After adjusting demographic variables and environmental co-pollutants, we built statistical models based on 424 couples and checked different air pollutants on their pregnancy outcomes. We find that Jinan is tightly surrounded by mountains from 3 of 4 sides, geographically resulting in a typical basin texture that hinders the diffusion of ambient pollutants. Of 424 pregnant women enrolled in this study, 17 subjects were diagnosed with preterm birth. Using air quality index (AQI) as an integrated indicator of PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃, we found that each interquartile range (IQR) increase in AQI was associated with 11% increased odds of preterm birth. Also, elevating PM_2.5, PM₁₀, SO₂, and O₃ led to different increased risk levels of preterm birth. By running the generalized additive model analyses, the association of AQI and preterm birth was further confirmed. In conclusion, based on samples in Jinan, east China, prenatal exposure to multiple ambient pollutants is associated with reduced gestational age and increased risk of preterm birth.

Assessment of the Factors Influencing Sulfur Dioxide Emissions in Shandong, China

Sulfur dioxide (SO2) is a serious air pollutant emitted from different sources in many developing regions worldwide, where the contribution of different potential influencing factors remains unclear. Using Shandong, a typical industrial province in China as an example, we studied the spatial distribution of SO2 and used geographical detectors to explore its influencing factors. Based on the daily average concentration in Shandong Province from 2014 to 2019, we explored the influence of the diurnal temperature range, secondary production, precipitation, wind speed, soot emission, sunshine duration, and urbanization rate on the SO2 concentration. The results showed that the diurnal temperature range had the largest impact on SO2, with q values of 0.69, followed by secondary production (0.51), precipitation (0.46), and wind speed (0.42). There was no significant difference in the SO2 distribution between pairs of sunshine durations, soot emissions, and urbanization rates. The meteorological factors of precipitation, wind speed, and diurnal temperature range were sensitive to seasonal changes. There were nonlinear enhancement relationships among those meteorological factors to the SO2 pollution. There were obvious geographical differences in the human activity factors of soot emissions, secondary production, and urbanization rates. The amount of SO2 emissions should be adjusted in different seasons considering the varied effect of meteorological factors.

Impact of the COVID-19 pandemic on air pollution in Chinese megacities from the perspective of traffic volume and meteorological factors

During 2020, the COVID-19 pandemic resulted in a widespread lockdown in many cities in China. In this study, we assessed the impact of changes in human activities on air quality during the COVID-19 pandemic by determining the relationships between air quality, traffic volume, and meteorological conditions. The megacities of Wuhan, Beijing, Shanghai, and Guangzhou were selected as the study area, and the variation trends of air pollutants for the period January-May between 2016 and 2020 were analyzed. The passenger volume of public transportation (PVPT) and the passenger volume of taxis (PVT) along with data on precipitation, temperature, relative humidity, wind speed, and boundary layer height were used to identify and quantify the driving force of the air pollution variation. The results showed that the change rates of fine particulate matter (PM_2.5), NO₂, and SO₂ before and during the lockdown in the four megacities ranged from -49.9% to 78.2% (average: -9.4% ± 59.3%), -55.4% to -32.3% (average: -43.0% ± 9.7%), and - 21.1% to 11.9% (average: -10.9% ± 15.4%), respectively. The response of NO₂ to the lockdown was the most sensitive, while the response of PM_2.5 was smaller and more delayed. During the lockdown period, haze from uninterrupted industrial emissions and fireworks under the effect of air mass transport from surrounding areas and adverse climate conditions was probably the cause of abnormally high PM_2.5 concentrations in Beijing. In addition, the PVT was the most significant factor for NO₂, and meteorology had a greater impact on PM_2.5 than NO₂ and SO₂. There is a need for more national-level policies for limiting firework displays and traffic emissions, as well as further studies on the formation and transmission of secondary air pollutants.

Vertical distributions of tropospheric SO2 based on MAX-DOAS observations: Investigating the impacts of regional transport at different heights in the boundary layer

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO₂ profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO₂ decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO₂ in different seasons might occur at different heights. The diurnal variations of SO₂ were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO₂ were observed in the morning for fall and winter, implying the accumulation or transport of SO₂ in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO₂ concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO₂ concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO₂ in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO₂ concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO₂ pollution.

Impacts of natural and socioeconomic factors on PM2.5 from 2014 to 2017

The State Council of China had issued the Air Pollution Prevention and Control Action Plan (abbreviated as "Clean Air Actions"), which ended in 2017. To evaluate the implementation effect of the clean air actions and provide the scientific basis on the future control policy, a Geographical Detector was used to quantify the impact of natural and socioeconomic factors on the PM_2.5 concentration and its reductions in China from the years of 2014-2017. In terms of the impact on PM_2.5 reduction, the industrial sulfur dioxide (SO₂) and industrial soot emissions are the only two factors shown significant influences. So the controls of industrial emission were the major policies during the implementation of the Clean Air Actions. In terms of the impact on the PM_2.5 concentrations, industrial emission was the strongest socioeconomic factor in the beginning of the Clean Air Actions, but its dominance was then declining. In contrast, the influences of population density had been enhancing and became the greatest factor in the final year. So the new control measures should focus on the urbanization regulation. In addition, the interactions between different socioeconomic factors are proved to bivariate enhance the influences on the PM_2.5 concentration levels. Multiple factors should thus be taken into account when any new control policies are going to be established.

Environmental investigation of pollutants in coal mine operation and waste dump area monitored in Ordos Region, China

The increasingly severe emissions of greenhouse and poisonous gases from environmentally unsafe stockpiled coal mine waste dumps have urged people from the academia as well as the industry to focus on environmental impact assessment. In this study, one-year air pollutant monitoring was conducted at the Qipanjing coalfield in Inner Mongolia of China for determining the distribution pattern statue of pollutant exposure and its main driving factors. We used FTIR spectroscopy to measure the inorganic compounds in particulate matter with a diameter of less than 2.5 μm. The spatial and temporal distribution characteristics of leading pollutants, including PM2.5, PM10, SO2, NO2, O3 and CO were analyzed. Firstly, the research showed that the temporal and spatial distribution of pollutants in the coal mine waste area is non-homogeneous. Secondly, some meteorological parameters, such as wind speed, relative humidity, temperature, and rainfall, were found to have significant effects on air pollutant distribution. Stable atmospheric conditions were unfavorable for the diffusion of pollutants and prolong the pollution process. Finally, in the vicinity of coalfields, SO2 and NO2 are present in high concentrations in air. Primary reasons for such high values are coal mining-related activities and active mine fires. This study will help to offer valuable and detailed information for understanding and interpreting the pollution source.

Spatial distribution and determinants of PM2.5 in China's cities: fresh evidence from IDW and GWR

While numerous studies have explored the spatial patterns and underlying causes of PM_2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM_2.5 factors. In order to enrich this research field, we collected PM_2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM_2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM_2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM_2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM_2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM_2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM_2.5. When the per capita income reaches 47,000 yuan, the PM_2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM_2.5 governance measures.

The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model

BACKGROUND

Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change.

OBJECTIVE

Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature.

METHOD

In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM_2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015.

RESULT

Cities with serious PM_2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country's southeast, where enhanced precipitation was also shown to reduce PM_2.5 concentrations; whilst in southern and central and western regions, precipitation and PM_2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM_2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM_2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM_2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM_2.5 concentration levels in southeastern cities than in other cities in China.

CONCLUSION

PM_2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM_2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.

Industrial structure, urban governance and haze pollution: Spatiotemporal evidence from China

As a negative external product of China's rapid development, haze pollution has seriously affected the quality of economic development and people's quality of life. This paper firstly explores the important reasons for the uncoordinated industrial structure caused by haze pollution, and puts forward the purpose of promoting the adjustment of industrial structure through urban governance in order to tackle with the urgent problem of haze pollution. Using panel data from 287 cities in China, this paper analyzes the relationship among industrial structure, urban governance and haze pollution using the Geographically and Temporally Weighted Regression (GTWR) model. The innovations are: (1) this paper focuses on the topic of industrial structure, urban governance and haze pollution simultaneously. (2) this paper uses the method of GTWR to comprehensively consider the spatial and temporal tendency at the same time. (3) Conclusions are helpful to provide targeted policy recommendations. And the results show that: (1) the spatial clustering characteristics of haze pollution are very prominent, and have been suppressed to a certain extent under the measures of urban governance; (2) the spatial and temporal differences of industrial structure on haze pollution are large; (3) corporate governance plays an important role in slowing down haze pollution; (4) in public governance, the green coverage rate of built-up areas, the innocuous disposal rate of domestic garbage and the increase of public transport will have a negative impact on haze pollution, while highly concentrated urban population, high level of economic development, large number of industrial enterprises above designated size, and increased thermal power generation capacity will increase the degree of haze pollution; (5) cities with steadily decreasing of the proportion of the secondary industry, the proportion of the tertiary industry, the comprehensive treatment rate of industrial solid materials, the green coverage rate of the built-up area and the industrial enterprises above designated size are mainly lie in southeastern China, respectively; and cities with decline in innocuous disposal rate of domestic garbage are concentrated in the western region, while cities with significant changes of the number of buses per unit are mainly distributed in the northeastern region, the other variables are not obvious.

Effects of socioeconomic and natural factors on air pollution in China: A spatial panel data analysis

China's energy use has increased significantly in recent years with the country's rapid economic growth and large-scale urbanization. Therefore, air pollution has become a major issue. In this study, we conducted spatial autocorrelation and spatial panel regression analyses of sulfur dioxide (SO₂) and nitrogen oxide (NO_X) emissions using the panel data of 31 provincial-level administrative units in China during the period 2011-2017 to comprehensively understand the factors affecting air pollutant emissions. This study contributes to the literature by considering comprehensive factors and spatial effects in the panel-data econometric framework of the whole country of China. The analysis of spatial characteristics shows that during the study period, pollutant emissions in China declined, although emissions in northern regions were still relatively high. Furthermore, SO₂ and NO_X emissions showed significant positive spatial autocorrelations. The results of a fixed-effect spatial lag model showed that both socioeconomic and natural factors were statistically significant for air pollutant emissions, although the degree differed by the type of pollutant. The population, the urbanization rate, the share of added value of secondary industry, and heating and cooling degree days positively affected emissions, while population density, per-capita gross regional product, precipitation, and relative humidity negatively affected emissions. Based on these results, we have put forward suggestions to address the issue of air pollution and achieve environmental sustainability, such as the promotion of regional cooperation and a transition of the economic structure.

Does asymmetric persistence in convergence of the air quality index (AQI) exist in China?

In recent years, China's air pollution has caused significant concern in the academia. China is the hub of business and financial activities, with the most populous cities. It is important to determine the convergence and asymmetric persistence of air quality index (AQI hereafter) in China to achieve sustainable development goals, especially the ones related to the environment. This paper uses the Fourier quantile unit root test to check for inter-regional convergence of monthly AQI for 74 cities across China from January 2013 to July 2019. For a comparative baseline analysis, five conventional univariate and quantile unit root tests are also conducted. The empirical outcomes show that the Fourier quantile unit test exhibits a significant advantage in detecting smooth breaks and evaluating the asymmetric behavior and mean-reverting properties of AQI. Moreover, the monthly AQI in 70 out of 74 C0hinese cities are stationary processes. These findings not only focus on the appropriate use of relevant modeling techniques of smooth breaks and asymmetries in the AQI series of the 74 Chinese cities but also provide crucial environmental sustainability and economic implications for AQI regulation policies.

Effects of the socio-economic influencing factors on SO2 pollution in Chinese cities: A spatial econometric analysis based on satellite observed data

The research on SO₂ pollution in China has been hotly debated over the past decades. Different from the existing studies, this work employs satellite observed SO₂ columns from 2005 to 2016 and applies a spatial econometric approach to investigate the socio-economic influencing factors of SO₂ pollution of 270 prefecture-level cities in China. The findings are as follows. (1) SO₂ pollution over China exhibits a significant and positive spatial autocorrelation. (2) The most polluted area is concentrated on the North China Plain. However, SO₂ pollution over China has been reduced gradually during the sample period, implying that overall environmental quality in China has been substantially improved. (3) Besides, the results of spatial econometric models are not in support of "pollution haven hypothesis". On the contrary, the pollution halo effect of foreign direct investment works well and contributes to reducing SO₂ pollution in China. Moreover, we find that urban economic levels and innovative capability are negatively correlated with SO₂ pollution, indicating that economic growth and an increase in innovation can help improve environmental quality. On contrast, the share of the secondary industry, urbanization and transportation are found to have positive impacts, indicating that they are three main contributors to SO₂ pollution in China.

Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.

Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression

PM_2.5 pollution is caused by multiple factors and determining how these factors affect PM_2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM_2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM_2.5 and the different factors varied with location. SO₂ emission positively affected PM_2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM_2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM_2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM_2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

Estimating historical SO2 level across the whole China during 1973-2014 using random forest model

Ambient SO₂ pollution poses a great threat on air quality, human health, and ecosystem safety. The ground-level SO₂ monitoring sites over China have been established during the past years, while the long-term SO₂ data was still missing before 2014, which cannot reveal the evolution trend of SO₂ pollution and assess its response to the anthropogenic activity. In this work, we developed a high-quality random forest (RF) model to simulate the long-term SO₂ concentration across the entire China from 1973 to 2014, based on substantial explanatory variables (e.g., meteorological factors, SO₂ emission intensity, land use types). The 10-fold cross-validation R² value and root mean square error (RMSE) over China reached 0.64 and 17.06 μg/m³, respectively, both of which were significantly higher than those of other models such as back propagation neural network (BPNN) and generalized regression neutral network (GRNN). Among all of the predictors, T displayed the highest relative importance value, followed by WS, Prec, SO₂ emission intensity, RH, DOY, elevation, and the lower one for land use types and P. The estimated mean SO₂ concentration during 1973-2014 displayed the remarkably spatial variation with the higher value in North China Plain (NCP) and Middle part of Inner Mongolia. This historical SO₂ level estimation suggested that air pollution was not a new environmental issue that could be dated back to 1973. Overall, the annually mean SO₂ level for each grid increased from 29.46 ± 9.79 to 31.44 ± 8.77 μg/m³ from 1973 to 2014. The annually mean SO₂ concentration in NCP showed rapid increase from 34.32 ± 3.05 to 36.97 ± 3.18 μg/m³ during 1973-2002, whereas they decreased significantly after 2003 (from 37.46 ± 3.20 to 36.13 ± 3.48 μg/m³ during 2003-2014). The gradual decrease since 2003 was benefitted from the adjustment of the energy consumption structure and the adoption of emission control technologies. However, the SO₂ levels in some western regions showed the violent increases since 2003 due to the proposal of "development of the western region". The estimated daily SO₂ concentration across the entire China could provide the essential data for epidemiological research and air pollution prevention.

Estimating the remaining atmospheric environmental capacity using a single-box model in a high pollution risk suburb of Chengdu, China

The atmospheric pollution has been the public attention in recent years. In order to better coordinate economic development and atmospheric environmental management, China introduced the concept of atmospheric environmental capacity (AEC). The remaining atmospheric environmental capacity (RAEC) calculated by existing atmospheric pollution sources and AEC is an important basis for regional development and environmental protection. The RAEC of the high-pollution risk suburb of Chengdu in 2015 was estimated by the single-box model and analyzed on multiple time scales. The results show that the RAEC of SO₂ and NO₂ in this region is 3299 t/a and 2849 t/a, respectively under the annual time scale. However, in the daily time scale, the RAEC of NO₂ is negative for 3 days, that is, there are 3 days with serious air pollution. Therefore, it is not appropriate to plan the industrial area only by relying on annual RAEC. Especially, RAEC displays inter-seasonal and monthly variability. On the one hand, in plain areas with low wind speed and little change in wind direction, achieving the prediction of atmospheric mixing layer height could give early warning of atmospheric pollution events. On the other hand, different management measures are taken on different time scales. On a long timescale, the regional energy structure should be optimized. On seasonal and monthly time scales, the production plans should be adapted to RAEC. On the daily time scale, it mainly deals with the serious atmospheric pollution accident timely.

The Convergence of Sulphur Dioxide (SO2) Emissions Per Capita in China

As the third-largest SO2 emitter in the world, China is facing mounting domestic and external pressure to tackle the increasingly serious SO2 pollution. Figuring out the convergence and persistence of sulfur dioxide (SO2) emissions matters much for environmental policymakers in China. This study mainly utilizes the Fourier quantile unit root test to survey the convergence of the SO2 emissions per capita in 74 cities of China during the period of December 2014 to June 2019, by conducting five traditional unit root tests and a quantile root unit test as a comparative analysis. The empirical results indicate that the SO2 emissions per capita in 72 out of 74 cities in China are convergent in the sample period. The results also suggest that the unit root behavior of the SO2 emissions per capita in these cities is asymmetrically persistent at different quantiles. For the cities with the convergent SO2 emissions, the government should consider the asymmetric mean-reverting pattern of SO2 emissions when implementing environmental protection policies at different stages. For Hefei and Nanjing, the local governments need to enact stricter environmental protection policies to control the emission of sulfur dioxide.

Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016

Multiyear spatiotemporal distributions of daily ambient sulfur dioxide (SO₂) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO₂ levels across China on 0.1^o grid from 2013 to 2016 by assimilating satellite- and ground-based SO₂ observations using the random-forest spatiotemporal kriging (RF-STK) model. The cross-validation R² is 0.64 and 0.81 for predicting the daily and multiyear averages, respectively. The multiyear population-weighted average of SO₂ for China is 28.1 ± 14.0 μg/m³, and the severest SO₂ pollution occurs in the northern China (45.1 ± 14.7 μg/m³). The SO₂ concentration shows a strong seasonality, i.e., highest in winter (41.6 ± 26.4 μg/m³) and lowest in summer (19.6 ± 8.3 μg/m³). During 2013-2016, the annual SO₂ decreases from 34.4 ± 18.2 to 22.7 ± 11.1 μg/m³, and the population% exposed for more than 100 nonattainment days (SO₂ > 20 μg/m³) drops from 86% to 48%. While the seasonality of SO₂ is mainly determined by the meteorological variation, the substantial decrease attributes to the reduced emissions such as from coal consumption. The effectiveness of SO₂ emission reduction varies widely in different prefectures of China. In Shandong province, the SO₂ concentration decreases by -45% while the coal consumption increases by 9%. In Shanxi province, the SO₂ concentration decreases by -15% while the coal consumption decreases by -3%. The contrasting effectiveness between these two provinces is associated with the much fewer waste gas disposal facilities in Shanxi than Shandong. Stricter regulation is required to further lower the SO₂ concentration in order to protect the public health, especially in the northern China.

The Spatiotemporal Dynamics and Socioeconomic Factors of SO2 Emissions in China: A Dynamic Spatial Econometric Design

With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO2 emissions to peak in China. By exploratory spatial data analysis (ESDA) and econometric approaches, this study explores the spatiotemporal characteristics of SO2 emissions and reveals how the socioeconomic determinants influence the emissions in China’s 30 provinces from 1995 to 2015. The study first identifies the overall space- and time-trend of regional SO2 emissions and then visualizes the spatiotemporal nexus between SO2 emissions and socioeconomic determinants through the ESDA method. The determinants’ impacts on the space–time variation of emissions are also confirmed and quantified through the dynamic spatial panel data model that controls for both spatial and temporal dependence, thus enabling the analysis to distinguish between the determinants’ long- and short-term spatial effects and leading to richer and novel empirical findings. The study emphasizes close spatiotemporal relationships between SO2 emissions and the socioeconomic determinants. China’s SO2 emissions variation is the multifaceted result of urbanization, foreign direct investment, industrial structure change, technological progress, and population in the short run, and it is highlighted that, in the long run, the emissions are profoundly affected by industrial structure and technology.

Environmental efficiency and equality embodied in China's inter-regional trade

Embodied emissions in trade have been widely studied; however, there is still a lack of studies that explore whether a country is benefitting from its inter-regional trade in terms of pollutant emissions. This study took sulfur dioxide (SO₂) emissions as an example and employed modified input-output (MIO) model and traditional input-output (IO) model to quantify emissions under no-trade and trade conditions, and further investigated environmental efficiency and equality of inter-regional trade in China in 2010. The results show that inter-regional trade had increased emissions by 28% compared to no-trade emissions, which confirms the environmental inefficiency of inter-regional trade in China. This was largely because regions with better technology and low emission intensities tended to outsource the production of pollution-intensive but low value-added goods to regions with high emission intensities through inter-regional trade. The exchanges of pollution-intensive products in inter-regional trade have led to notable environmental inequities. Eastern regions usually gained the greatest environmental benefits from trade, while central regions (especially Shanxi, Henan, and Hebei) suffered the largest environmental loss induced by trade. Specifically, Guangdong plundered other regions the most (796 G gram (Gg)), while Shanxi was plundered the most by other regions (790 Gg). Polices to differentiate reduction criteria for emission intensity in different regions and adjust trade patterns within China could be recommended in order to achieve trade-related environmental efficiency as well as environmental equality.

A framework for investigating the air quality variation characteristics based on the monitoring data: Case study for Beijing during 2013-2016

In this study, an analysis framework based on the regular monitoring data was proposed for investigating the annual/inter-annual air quality variation and the contributions from different factors (i.e., seasons, pollution periods and airflow directions), through a case study in Beijing from 2013 to 2016. The results showed that the annual mean concentrations (MC) of PM_2.5, SO₂, NO₂ and CO had decreased with annual mean ratios of 7.5%, 28.6%, 4.6% and 15.5% from 2013 to 2016, respectively. Among seasons, the MC in winter contributed the largest fractions (25.8%~46.4%) to the annual MC, and the change of MC in summer contributed most to the inter-annual MC variation (IMCV) of PM_2.5 and NO₂. For different pollution periods, gradually increase of frequency of S-1 (PM_2.5, 0~75 μg/m³) made S-1 become the largest contributor (28.8%) to the MC of PM_2.5 in 2016, it had a negative contribution (-13.1%) to the IMCV of PM_2.5; obvious decreases of frequencies of heavily polluted and severely polluted dominated (44.7% and 39.5%) the IMCV of PM_2.5. For different airflow directions, the MC of pollutants under the south airflow had the most significant decrease (22.5%~62.5%), and those decrease contributed most to the IMCV of PM_2.5 (143.3%), SO₂ (72.0%), NO₂ (55.5%) and CO (190.3%); the west airflow had negative influences to the IMCV of PM_2.5, NO₂ and CO. The framework is helpful for further analysis and utilization of the large amounts of monitoring data; and the analysis results can provide scientific supports for the formulation or adjustment of further air pollution mitigation policy.

Analysis of influential factors on air quality from global and local perspectives in China

Regional haze pollution has frequently occurred in China over the past several years, and this haze has hindered the development of the economy and harmed the health of people in China. Currently, several studies have analyzed the impact of different influencing factors on haze. However, few studies have comprehensively analyzed the influential factors of haze from different perspectives. In this paper, we utilized global and local regression models to explore the main influential factors on air quality index (AQI) in China from global and local perspectives. The results are as follows: (1) the AQIs of Chinese cities have significant positive spatial correlation, and higher values of AQI were typically found in Beijing-Tianjin-Hebei, Shandong, Henan, Shanxi and Shaanxi Province; (2) from a global perspective, as there is one unit of increase in the average AQI of one city's neighbors, the city's AQI will increase by 0.827 unit. An increase in the industrial structures and the number of civilian vehicles will also lead to an increase in the AQI, but the impact of precipitation is reversed; and (3) from a local perspective, there are spatial differences in the effects of different factors on the AQI. In northern China, an appropriate temperature reduction and an appropriate increase in atmospheric pressure is helpful for reducing haze pollution; however, opposing conditions are found in southern China. Compared with China's coastal cities, the increase in precipitation is more effective at reducing the AQI in inland cities. Compared with other cities, reducing the industrial structure and the number of civilian vehicles was more effective for haze management in Beijing, Tianjin, Shandong, Henan, Shanxi, and Shaanxi provinces. These results of this paper are helpful for government departments to formulate regionally differentiated governance policies regarding haze.

Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling

Whilst numerous studies have explored the spatial patterns and underlying causes of PM_2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM_2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM_2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM_2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM_2.5 in 95.60% of the cities and it correlated negatively with PM_2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM_2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM_2.5, revealing that increasing urban fragmentation was conducive to PM_2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM_2.5 governances.

The effect of natural and anthropogenic factors on PM2.5: Empirical evidence from Chinese cities with different income levels

The aim of this paper is to estimate the effects of natural conditions and anthropogenic factors on PM_2.5 concentrations, taking into consideration differences in the income levels, and thus the development stages, of the cities studied. To achieve this goal, a balanced dataset of 287 Chinese cities was divided into different income-based panels for the period 1998-2015. The empirical estimation results indicated that meteorological conditions exerted varied effects on PM_2.5 concentrations across different income-based panels. The results show that the coefficients of temperature were positive and significant in all panels, with the exception of upper-middle-income cities. Whilst wind speed and precipitation were found to be conducive to reducing PM_2.5 concentrations, no such significant correlation was found in relation to relative humidity (except in high-income cities). In terms of the anthropogenic factors addressed in the study, we found an inverted U-shaped relationship between economic development and PM_2.5 concentrations, confirming the Environmental Kuznets Curve hypothesis. In addition, the industrial structure and road density were observed to exert significant positive impacts on PM_2.5 concentrations. The empirical analysis of the effects of FDI on PM_2.5 concentrations indicate that FDI aggravated PM_2.5 pollutions in the total cities and lower-middle-income cities panels, supporting the Pollution Haven Hypothesis. The empirical results for population density suggested that it does not significantly influence PM_2.5 concentrations. Moreover, we found that built-up area exerts mixed effects on PM_2.5 concentrations. These results cast a new light on the issue of PM_2.5 pollution for government policy makers tasked with formulating measures to mitigate the concentration of such pollutants, encouraging that consideration be given to the differences between cities with different income levels.

CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China

This study aims to test the environmental Kuznets curve (EKC) for carbon dioxide (CO₂) emissions in China by developing a new framework based on the suggestion of Narayan and Narayan (2010). The dynamic effect of natural gas and renewable energy consumption on CO₂ emissions is also analyzed. Considering the structural break observed in the sample, a series of econometric techniques allowing for structural breaks is utilized for the period 1965-2016. The empirical results confirm the existence of the EKC for CO₂ emissions in China. Furthermore, in both the long-run and the short-run, the beneficial effects of natural gas and renewables on CO₂ emission reduction are observable. In addition, the mitigation effect of natural gas on CO₂ emissions will be weakened over time, while renewables will become progressively more important. Finally, policy suggestions are highlighted not only for mitigating CO₂ emissions, but also for promoting growth in the natural gas and renewable energy industries.

The temporal variation of SO2 emissions embodied in Chinese supply chains, 2002-2012

Whilst attention is increasingly being focused on embodied pollutant emissions along supply chains in China, relatively little attention has been paid to dynamic changes in this process. This study utilized environmental extended input-output analysis (EEIOA) and structural path analysis (SPA) to investigate the dynamic variation of the SO₂ emissions embodied in 28 economic sectors in Chinese supply chains during 2002-2012. The main conclusions are summarized as follows: (1) The dominant SO₂ emission sectors differed under production and consumption perspectives. Electricity and heat production dominated SO₂ emissions from the point of view of production, while construction contributed most from the consumption perspective. (2) The embodied SO₂ emissions tended to change from the path (staring from consumption side to production side): "Services→Services→Power" in 2002 to the path: "Construction and Manufacturing→Metal and Nonmetal→Power" in 2012. (3) Metal-driven emissions raised dramatically from 15% in 2002 to 22% in 2012, due to increasing demand for metal products in construction and manufacturing activities. (4) Power generation was found to result in the greatest volume of production-based emissions, a burden it tended to transfer to upstream sectors in 2012. Controlling construction activities and cutting down end-of-pipe discharges in the process of power generation represent the most radical interventions in reducing Chinese SO₂ emissions. This study shed light on changes in SO₂ emissions in the supply chain, providing a range of policy implications from both production and consumption perspectives.

Incorporating temperature effects in California's on-road emission gridding process for air quality model inputs

On-road mobile sources play a significant role in air quality modeling and these models require gridded, hourly emission inputs. Due to its geographical and meteorological diversity and stringent air quality regulations, California state always poses big challenge for air quality modelers and policy makers. At the same time, the impact of ambient temperature on vehicle emissions has been well researched in the past few decades and it is vital to prepare a reliable on-road gridded emission inventory for air quality modeling. This technical paper introduces a gridding method that takes temperature impacts into account, calculates emissions from grid-level to county, and attempts to quantify the likely effects of such a bottom-up, temperature sensitive approach on a gridded on-road emission inventory. To provide confidence in the proposed SMOKE-EMFAC method, a detailed analysis was carried out to compare the results with the default EMFAC output, and the results were within ±1%. Applying detailed grid level temperatures, we also found that criteria pollutant distributions are sensitive to them, and they are in accordance with previous US-EPA study. The proposed method could be very useful while testing different complex emission regulations and policies due to its inherent flexibility.

Examining the effects of socioeconomic development on fine particulate matter (PM2.5) in China's cities using spatial regression and the geographical detector technique

The frequent occurrence of extreme smog episodes in recent years has begun to present a serious threat to human health. In addition to pollutant emissions and meteorological conditions, fine particulate matter (PM_2.5) is also influenced by socioeconomic development. Thus, identifying the potential effects of socioeconomic development on PM_2.5 variations can provide insights into particulate pollution control. This study applied spatial regression and the geographical detector technique for assessing the directions and strength of association between socioeconomic factors and PM_2.5 concentrations, using data collected from 945 monitoring stations in 190 Chinese cities in 2014. The results indicated that the annual average PM_2.5 concentrations is 61±20μg/m³, and cites with more than 75μg/m³ were mainly located in North China, especially in Tianjin and Hebei province. We also identified a marked seasonal variation in concentrations levels, with the highest level in winter due to coal consumption, lower temperatures, and less rainfall than in summer. Monthly variations followed a "U-shaped" pattern, with a down trend from January and an inflection point in September and then an increasing trend from October. The results of spatial regression indicated that population density, industrial structure, industrial soot (dust) emissions, and road density have a significantly positive effect on PM_2.5 concentrations, with a significantly negative influence exerted only by economic growth. In addition, trade openness and electricity consumption were found to have no significant impact on PM_2.5 concentrations. Using the geographical detector technique, the strength of association between the five significant drivers and PM_2.5 concentrations was further analyzed. We found notable differences among the variables, with industrial soot (dust) emissions playing a greater role in the PM_2.5 concentrations than the other variables. These results will be helpful in understanding the dynamics and the underlying mechanisms at work in PM_2.5 concentrations in China at the city level, and thereby assisting the Chinese government in employing effective strategies to tackle pollution.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.