A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions

Quantifying the impacts of Canadian wildfires on regional air pollution networks

Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.

Air Pollution-Related Neurotoxicity Across the Life Span

Air pollution is a complex mixture of gases and particulate matter, with adsorbed organic and inorganic contaminants, to which exposure is lifelong. Epidemiological studies increasingly associate air pollution with multiple neurodevelopmental disorders and neurodegenerative diseases, findings supported by experimental animal models. This breadth of neurotoxicity across these central nervous system diseases and disorders likely reflects shared vulnerability of their inflammatory and oxidative stress-based mechanisms and a corresponding ability to produce brain metal dyshomeo-stasis. Future research to define the responsible contaminants of air pollution underlying this neurotoxicity is critical to understanding mechanisms of these diseases and disorders and protecting public health.

Research on the Spatial Heterogeneity and Influencing Factors of Air Pollution: A Case Study in Shijiazhuang, China

Rapid urbanization causes serious air pollution and constrains the sustainable development of society. The influencing factors of urban air pollution are complex and diverse. Multiple factors act together to interact in influencing air pollution. However, most of the existing studies on the influencing factors of air pollution lack consideration of the interaction mechanisms between the factors. Using multisource data and geographical detectors, this study analyzed the spatial heterogeneity characteristics of air pollution in Shijiazhuang City, identified its main influencing factors, and analyzed the interaction effects among these factors. The results of spatial heterogeneity analysis indicate that the distribution of aerosol optical depth (AOD) has obvious agglomeration characteristics. High agglomeration areas are concentrated in the eastern plain areas, and low agglomeration areas are concentrated in the western mountainous areas. Forests (q = 0.620), slopes (q = 0.616), elevation (q = 0.579), grasslands (q = 0.534), and artificial surfaces (q = 0.506) are the main individual factors affecting AOD distribution. Among them, natural factors such as topography, ecological space, and wind speed are negatively correlated with AOD values, whereas the opposite is true for human factors such as roads, artificial surfaces, and population. Each factor can barely affect the air pollution status significantly alone, and the explanatory power of all influencing factors showed an improvement through the two-factor enhanced interaction. The associations of elevation ∩ artificial surface (q = 0.625), elevation ∩ NDVI (q = 0.622), and elevation ∩ grassland (q = 0.620) exhibited a high explanatory power on AOD value distribution, suggesting that the combination of multiple factors such as low altitude, high building density, and sparse vegetation can lead to higher AOD values. These results are conducive to the understanding of the air pollution status and its influencing factors, and in future, decision makers should adopt different strategies, as follows: (1) high-density built-up areas should be considered as the key areas of pollution control, and (2) a single-factor pollution control strategy should be avoided, and a multi-factor synergistic optimization strategy should be adopted to take full advantage of the interaction among the factors to address the air pollution problem more effectively.

Assessment of water quality under various environmental features using a site-specific weighting water quality index

In a multiregional river system, environmental features such as natural conditions and anthropogenic activities vary among regions, resulting in spatiotemporal variations in water quality. Therefore, a robust water quality assessment method (e.g., water quality index [WQI]) that considers various environmental features is essential for water resources management. This study developed a min/max autocorrelation factor analysis (MAFA) based WQI framework (MAFAWQI). The statistical procedure reduces the bias of expert opinions. The MAFAWQI characterizes impaired water quality variables as indicators and assesses appropriate weighting values of indicators at each sampling site to reflect site-specific environmental features. The MAFAWQI was successful for assessing water quality in the middle and down streams of Han River in central China with site-specific pollution features such as nitrogen and phosphorus pollution related to multiple-source in tributaries, impacts of tributaries on the main stream, and phosphorus pollution related to nonpoint-source in agricultural regions. The MAFAWQI exhibited a balanced rating of water quality compared to the strict assessment method using a single indicator and the lenient assessment method using stationary weighting values of indicators. The MAFAWQI scores indicated that the water quality in tributaries and during the spring were significantly worse than those in and during the other regions and seasons in the middle and down streams of Han River, respectively. The framework and application of the MAFAWQI may provide a new perspective for developing WQIs.

Spatial-temporal characteristics of urban air pollution in 337 Chinese cities and their influencing factors

Urban air pollution, especially in the form of haze events, has become a serious threat to socio-economic development and public health in most developing countries. It is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of our study is to develop consistent methodologies for constructing an index system and for assessing the influencing factors of the urban air pollution occurrence based on the Driver-Pressure-State-Impact-Response (DPSIR) framework by incorporating spatial analysis, geographical detector, and geographically weighted regression models. The 27 influencing factors were selected for assessing their influences on the urban air pollution occurrence in 337 Chinese cities. The results indicate that the spatial pattern of the urban air pollution in China was mostly consistent with the Chinese population-based Hu Line. Urban air pollution frequently occurred in North China, Central China, Northeast China, and East China, and displayed strong seasonality. The influencing factors of urban air pollution were complex and diverse, varying from season to season. Influencing factor analysis also shows that the explanatory power between any two influencing factors was greater than that of a single influencing factor of the urban air pollution. Furthermore, most influencing factors had both positive and negative effects and local effects on urban air pollution. Finally, we put forward five suggestions on reducing urban air pollution occurrence, which can provide the basis and reference for the government to make policies on urban air pollution control in China.

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan's Government enforced in 2017, "suspended particulate matter" has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.

Spatiotemporally mapping of the relationship between NO2 pollution and urbanization for a megacity in Southwest China during 2005-2016

Nitrogen dioxide (NO₂) significantly contributes to air pollution. Long-term NO₂ exposure is harmful to human health. The NO₂ pollution in China has surpassed developed countries and attracts international attention. To understand the spatial and temporal distributions of NO₂ across Chengdu in Southwest China, a random forest (RF) model was developed based on NO₂ environmental monitoring data, the Ozone Monitoring Instrument (OMI) satellite retrievals, and geographic covariates. The RF model showed good performance with a cross validation R² of 0.77, and a root mean square error (RMSE) of 11.0 μg/m³. The ground-level NO₂ concentrations of Chengdu for 2005-2016 were predicted using the developed model with the multiyear population weighted NO₂ concentration being 41.7 ± 11.7 μg/m³. The predicted NO₂ concentrations exhibited a clear seasonal variation trend with winter being the highest and summer being the lowest. Furthermore, higher NO₂ concentrations in the downtown areas were observed than that in the rural areas indicating the former being attributed to more anthropogenic sources. The population weighted NO₂ concentrations with deseasonlization were relatively high during 2011-2013. The NO₂ concentration increased at a rate of 0.81 μg/m³/year before 2011 (43.4 ± 11.2 μg/m³) and decreased at a rate of -1.03 μg/m³/year after 2013 (44.8 ± 12.8 μg/m³).