Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China

Understanding the drivers of PM2.5 concentrations in Chinese cities: A comprehensive study of anthropogenic and environmental factors

Understanding the factors that drive PM2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics.

The effects of surface vegetation coverage on the spatial distribution of PM2.5 in the central area of Nanchang City, China

The frequent occurrence of haze has caused widespread concern in China, and PM2.5 is thought to be the main cause. Previous research showed that PM2.5 was not only influenced by meteorological conditions but also by land cover especially surface vegetation. It was concluded that PM2.5 concentration is significantly influenced by surface vegetation, but spatially how and in what manner are still unanswered. Taking the central area of Nanchang City, China, as a case, this study firstly applied land use regression (LUR) model to simulate the distribution of PM2.5 in 2020. Then, the dichotomous model was used to determine vegetation coverage. A statistical regression model was used to analyze the influence of vegetation cover on PM2.5 and the scale effects. The results showed that (1) vegetation coverage and PM2.5 concentration were both significantly negatively correlated at the spatial scales selected for this study. (2) The effect of vegetation coverage on PM2.5 varied with season and the 500 m had the best correlation. (3) The non-linear regression model fits better than the linear model, and the effect of vegetation coverage on PM2.5 was complex. (4) The effect of vegetation coverage on PM2.5 concentration was different with PM2.5 concentration level. The higher the PM2.5 concentration, the more pronounced the effect of vegetation coverage on it. This study proposed the idea and method of coupling vegetation coverage with PM2.5 concentration at the regional scale from gradient landscape's point of view and provided some references for mitigating PM2.5 pollution through optimizing urban vegetation patterns.

Locally weighted total least-squares variance component estimation for modeling urban air pollution

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM_2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method's efficiency in PM_2.5 modeling.

Has China's Low-Carbon City Construction Enhanced the Green Utilization Efficiency of Urban Land?

China has implemented the low-carbon city pilot (LCCP) policy in the hopes of efficiently limiting carbon emission intensity to combat global warming and promote green economic growth. Urban land utilization, the second-largest source of carbon emissions, is key to the LCCP policy being able to have the desired effect, which has attracted widespread attention. Based on the panel data from prefecture-level cities in China from 2006 to 2019, this study used the propensity score matching difference-in-differences method (PSM-DID) to examine the impacts of LCCP policy on green utilization efficiency of urban land (GUEUL). The results reveal that LCCP policy has a beneficial impact on GUEUL and can effectively boost the future possibilities of green and low-carbon city development. Due to variances in regional economic and resource endowment level, the impacts of LCCP are different. The pilot has pushed GUEUL in the eastern region, western region, and growing resource-based cities, but has failed to improve GUEUL in other regions. Policymakers should adhere to the long-term sustainability of the LCCP policy and adopt differentiated action strategies to promote GUEUL when implementing it in different regions.

Estimating PM2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China

With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM_2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM_2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM_2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM_2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM_2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM_2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM_2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R²) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 μg/m³ and 10.07 μg/m³, respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM_2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM_2.5 pollution, and the spatial spillover effect of PM_2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM_2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM_2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities.

Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10-5m-1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess changes in PM2.5 concentrations. The mean annual PM2.5 across the fixed sites ranged from 26 μg m-3 at a peri-urban site to 43 μg m-3 at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM2.5 levels (mean: 37 μg m-3), followed by high-density residential neighborhoods (mean: 36 μg m-3), while peri-urban areas recorded the lowest (mean: 26 μg m-3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 μg m-3) compared to non-Harmattan season (mean PM2.5: 23 μg m-3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 μg m-3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.

Effects of DNA Damage and Oxidative Stress in Human Bronchial Epithelial Cells Exposed to PM2.5 from Beijing, China, in Winter

Epidemiological studies have corroborated that respiratory diseases, including lung cancer, are related to fine particulate matter (<2.5 μm) (PM_2.5) exposure. The toxic responses of PM_2.5 are greatly influenced by the source of PM_2.5. However, the effects of PM_2.5 from Beijing on bronchial genotoxicity are scarce. In the present study, PM_2.5 from Beijing was sampled and applied in vitro to investigate its genotoxicity and the mechanisms behind it. Human bronchial epithelial cells 16HBE were used as a model for exposure. Low (67.5 μg/mL), medium (116.9 μg/mL), and high (202.5 μg/mL) doses of PM_2.5 were used for cell exposure. After PM_2.5 exposure, cell viability, oxidative stress markers, DNA (deoxyribonucleic acid) strand breaks, 8-OH-dG levels, micronuclei formation, and DNA repair gene expression were measured. The results showed that PM_2.5 significantly induced cytotoxicity in 16HBE. Moreover, the levels of reactive oxygen species (ROS), malondialdehyde (MDA), and cellular heme oxygenase (HO-1) were increased, and the level of glutathione (GSH) was decreased, which represented the occurrence of severe oxidative stress in 16HBE. The micronucleus rate was elevated, and DNA damage occurred as indicators of the comet assay, γ-H2AX and 8-OH-dG, were markedly enhanced by PM_2.5, accompanied by the influence of 8-oxoguanine DNA glycosylase (OGG1), X-ray repair cross-complementing gene 1 (XRCC1), and poly (ADP-ribose) polymerase-1 (PARP1) expression. These results support the significant role of PM_2.5 genotoxicity in 16HBE cells, which may occur through the combined effect on oxidative stress and the influence of DNA repair genes.