Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales: The Case of Northern China

Interaction patterns between economic growth and atmospheric environment in China under the "carbon neutrality" target

Clarifying the interaction patterns between economic growth and atmospheric environment (EG-AE) in China is important to achieve the "carbon neutrality" target. A conceptual framework of air pollutant emission in urban economic growth (APEUEG) was proposed to explore the interaction patterns in China from 2007 to 2017. The empirical analysis revealed that a N-shaped EKC exists between aerosol optical depth (AOD) and gross domestic product (GDP), with inflection points of $5000 and $27,000, respectively. Therefore, we speculated that when GDP per capita of a city exceeded $5000, the AOD gradually decreased. However, when GDP per capita of a city gained over $27,000, the economic growth and the atmospheric environment would be coordinated steadily. The interaction of EG-AE experienced three stages-pollution, improvement, and coordination-in China. Spatially, the interaction patterns of EG-AE presented five clusters, which were associated with the spatial distribution of city levels. China's prefecture-level cities have undergone the cluster of low AOD-low GDP (LL), the cluster of high AOD-high GDP (HH), and the cluster of low AOD-high GDP (LH), as urban level improves. By 2017, about 44% of Chinese cities had not completed the coordinated development yet. We found that policymakers should formulate differentiated urban greener economic development policies to reduce APEUEG.

A novel assessment framework for improving air quality monitoring network layout

Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai's AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs.Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.

An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.

Zero-impact emission limits of enterprise-scale air pollutants-a case study of a typical petrochemical enterprise in Shanghai Chemical Industry Park

The implementation of pollutant emission control has made initial achievements in the plant power, iron, and steel industries in China. To further improve air quality, it is of great significance to carry out research on zero-impact emissions of the petrochemical industry. Based on the existing concept and practice of zero emissions, this study proposes the concept of zero-impact emissions, taking emission concentration as the constraint. A typical petrochemical enterprise (namely Enterprise A) in Shanghai Chemical Industry Park as the research object, and used the CALPUFF model to simulate the target pollutant emissions (i.e. sulfur dioxide (SO₂), nitrogen oxide (NO_x), particulate matter (PM), and volatile organic compounds (VOCs)). The current emission standard, spatial distributions, and emission heights of chimneys in Enterprise A was considered as the baseline emission scenario and taking the zero-impact emission as a target for simulation. The results show that the current emission standards of NO_x and VOCs (benzene) exceeded the zero-impact emission limits, and needed to be reduced by 22% and 87.5%, respectively. Moreover, the areas that exceeded the zero-impact concentration limits were located northwest of the chimneys and Hangzhou Bay. In terms of seasonal effects, the wind conditions in spring were more adverse for the enterprise to achieve zero-impact emissions. Based on the simulation, the zero-impact emission limits of SO₂, NO_x, PM, and VOCs (benzene) for Enterprise A were 50 mg/m³, 78 mg/m³, 10 mg/m³, and 0.32 mg/m³, respectively.Implications: Through case study, this paper solves the environmental management issue which is of universal significance for chemical industry park. The concept of zero-impact emissions and the determination method of zero-impact concentration limits proposed in this study could be used as references for related research on cutting emissions. Although the conclusion of this study about the emission limits is not suitable for other enterprises to apply directly, the calculation method of zero-impact emission limit can be used by other enterprises. Furthermore, the zero-impact emission limits on park scale can be determined after comprehensive evaluation based on the calculation results of multiple enterprises.

Airborne Microplastic Concentrations in Five Megacities of Northern and Southeast China

Airborne microplastics (MPs) are receiving increasing attention due to their ubiquitous nature and the potential human health consequences resulting from inhalation. The limited data for airborne MP concentrations vary widely among studies (∼4 orders of magnitude), but comparisons are tenuous due to the inconsistent collection and detection/enumeration methodologies among studies. Herein, we used uniform methodologies to obtain comparable airborne MP concentration data to assess MP exposure intensity in five Chinese megacities. Airborne MP concentrations in northern cities (358 ± 132 items/m³) were higher than those in southeast cities (230 ± 94 items/m³) but of a similar order of magnitude, unlike previous studies. The majority (94.7%) of MPs found in air samples were smaller than 100 μm, and the main shape of airborne MPs was fragments (88.2%). Polyethylene, polyester, and polystyrene were the dominant polymers comprising airborne MPs. No consistent relationships were detected between airborne MP concentration and typical socioeconomic indices, and the spatial and diurnal patterns for airborne MPs were different from various components of air quality indices (PM_2.5, PM₁₀, etc.). These findings reflect the contrasting source/generation dynamics between airborne MPs and other airborne pollutants. Maximum annual exposure of humans to airborne MPs was estimated in the range of 1-2 million/year in these megacities, highlighting the need for additional research examining the human health risks from the inhalation of airborne MPs.

AQI time series prediction based on a hybrid data decomposition and echo state networks

A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature selection, and ESN (echo state network) neural network. Firstly, EWT (empirical wavelet transform) and VMD (variational mode decomposition) are used to decompose the original AQI time series into several stable and reliable subseries. Then, the ICA is used to select features of the above subseries for the ESN prediction model. Finally, the optimized feature variables are put into the ESN deep network to establish a prediction model of each AQI subseries and obtain the future AQI index. According to the experimental results of the daily AQI series in Beijing, Tianjin, and Shijiazhuang, we find that (a) among all decomposition methods, the proposed secondary decomposition method (EWT-SE-VMD) performs best in processing data; (b) it is proved that the proposed hybrid model has broad application prospect and research value in the AQI prediction field.

Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan

Chemical compositions of atmospheric fine particles like PM2.5 prove harmful to human health, particularly to cardiopulmonary functions. Multifaceted health effects of PM2.5 have raised broader, stronger concerns in recent years, calling for comprehensive environmental health-risk assessments to offer new insights into air-pollution control. However, there have been few studies adopting local air-quality-monitoring datasets or local coefficients related to PM2.5 health-risk assessment. This study aims to assess health effects caused by PM2.5 concentrations and metal toxicity using epidemiological and toxicological methods based on long-term (2007-2017) hourly monitoring datasets of PM2.5 concentrations in four cities of Taiwan. The results indicated that (1) PM2.5 concentrations and hazardous substances varied substantially from region to region, (2) PM2.5 concentrations significantly decreased after 2013, which benefited mainly from two actions against air pollution, i.e., implementing air-pollution-control strategies and raising air-quality standards for certain emission sources, and (3) under the condition of low PM2.5 concentrations, high health risks occurred in eastern Taiwan on account of toxic substances adsorbed on PM2.5 surface. It appears that under the condition of low PM2.5 concentrations, the results of epidemiological and toxicological health-risk assessments may not agree with each other. This raises a warning that air-pollution control needs to consider toxic substances adsorbed in PM2.5 and region-oriented control strategies are desirable. We hope that our findings and the proposed transferable methodology can call on domestic and foreign authorities to review current air-pollution-control policies with an outlook on the toxicity of PM2.5.

Effect of the wetland environment on particulate matter and dry deposition

In Beijing, particulate matter (PM) in the atmosphere, especially PM_2.5 and PM₁₀, have attracted public attention because of its adverse effects. A series of studies have investigated the sources and spatial-temporal variation of PM. Wetland has been reported to own the capacity of resolving air problem. To examine the characteristics of the particulate matter in wetlands, the diurnal variation of PM_2.5 and PM₁₀ concentrations with respect to two heights (i.e. 1.5 and 10 m, respectively) and three meteorological factors (i.e. wind speed, temperature, and relative humidity, respectively) was monitored in the Cuihu National Wetland Park in Beijing, and the dry deposition velocity and flux were analysed using the above-mentioned data. Results indicated that (1) As for diurnal variation, the PM concentration constantly decreased at 07:00-16:00 and gradually increased at 16:00-18:00. The maximum instantaneous concentration was observed at 07:00-10:00, while the minimum instantaneous concentration was observed at 13:00-16:00. (2) The annual concentration variation of PM followed the order of dry period > wet period > normal period. (3) The particulate concentrations at 10 m were always greater than those at 1.5 m. (4) The PM concentration was positively correlated to the relative humidity and negatively correlated to the temperature. Wind speed exhibited a complex effect on PM concentration. (5) The regulation of dry deposition efficiency followed the order of spring > winter > summer. (6) Wind speed strongly and positively affected the dry deposition velocity of PM10. The effects of temperature and relative humidity on dry deposition were uncertain.

Evaluate Air Pollution by Promethee Ranking in Yangtze River Delta of China

A series of problems that are related to population, resources, environment, and ecology have emerged in recent years with the advancement of industrialization and urbanization in China. Especially, air pollution has become a severe trouble that directly endangers the health of residents. Accordingly, it is a need to make the assessment of air quality among cities, so that corresponding measures can be taken. For this purpose, ten major cities are selected as the research objects in Yangtze River Delta. Additionally, this study gathers and processes the data of five main air pollutants PM_2.5, PM₁₀, SO₂, O₃, and NO₂, respectively. Furthermore, the maximizing deviation method is used to obtain the respective weight of these pollutants and the preference ranking organization method for enrichment evaluations (PROMETHEE) is introduced into the assessment of air quality among ten cities. As a result, the ranking of air quality in Ningbo, Wenzhou, Shanghai, and Shaoxing was at the fore from 2014 to 2017. Meanwhile, the performance of Ningbo has always kept the top two and Shaoxing’s ranking has risen since 2015. In addition, the air quality of Changzhou, Suzhou and Hangzhou was at an average level in the past four years. Moreover, the performance of Nanjing, Wuxi, and Zhenjiang was terrible when compared to other cities. Some useful suggestions have been proposed to control air quality based on the ranking results.

A Study of the Socioeconomic Forces Driving Air Pollution Based on a DPSIR Model in Henan Province, China

The 3D spatiotemporal distribution (spatial and annual-month-daily temporal) features of the air quality index (AQI), air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h), and air pollution risks (R) from 2003 to 2018 were investigated to understand the severity of air pollution in Henan province. The multiyear ascending trends for AQI and R values, with a peak in 2015, were observed in 2003 to 2018 since the annual population-weighted average concentrations of PM2.5, PM10, NO2, and O3-8h were always higher than the CAAQs II from 2013 to 2017 in Henan province. Changes in the monthly moving average AQI values in Henan province conformed to a U-shaped pattern, with the highest values in the winter (from December to February) and the lowest in the summer (from June to August). Triple peaks for AQI values of 8:00‒10:00 a.m., 6:00‒8:00 p.m., and 2:00‒4:00 p.m. in the representative municipalities corresponded with the morning and evening traffic tendencies and photochemical process. A spatial analysis indicated that there were decreasing trends for air pollution from northwest to southeast in Henan province. Data on 48 concrete parameters were collected from 2003 to 2017 to construct a driving force‒pressure‒state‒influence‒response (DPSIR) model for assessing the socioeconomic forces driving air pollution in this province. It was the too-rapid growth of the driving force index (DFI), induced by urban development and population growth (UDPG), economic growth and change of industrial structure (EGCIS), and energy consumption growth and structure change (ECGSC), that led to a direct increase in the atmospheric pollution burden, i.e., total emissions from air pollution and industrial emissions, which are linearly correlated to values of UDPG and ECGSC, respectively (p < 0.05). Furthermore, the prediction models for AQI and R values in Henan province, with the growth rates being 4.251 DFI−1 and 0.0816 DFI−1, respectively, were simulated by multiple linear regression analysis. Therefore, the integrated risks of air pollution in Henan province were originally driven by DFI.

Gaseous and particulate pollutants in Lhasa, Tibet during 2013-2017: Spatial variability, temporal variations and implications

In recent decades, most big cities in China have experienced severe air pollution accompanied by rapid economic and social development. Analysis of measurements of air pollutants form a fundamental basis for understanding the characteristics of air pollution and are important references for policy-making. In this study, five-year measurements of air pollutants at 6 sites in Lhasa, a typical high altitude big city in southwestern China, were analyzed from January 2013 to December 2017. Air pollutants at all the 6 sites in Lhasa generally displayed similar patterns of both diurnal and monthly variations, indicating the mixed atmospheric environment and the overall effect of the meteorological conditions in the city. Spatially, the air pollutant concentrations at the 6 sites were generally characterized by high concentrations of SO₂, NO₂, CO, PM₁₀ and PM_2.5 at urban sites and high O₃ concentrations at suburban sites. In comparison with other provincial capital cities in China, Lhasa has low concentrations of air pollutants, except for O₃, and thus, better air quality. Although Lhasa has experienced rapid urbanization and economic development, air pollution conditions have remained rather stable and even decreased slightly in term of particular air pollutants. We suggested that the relatively isolated location, low air pollutant emissions associated with its industrial structure and renewable energy consumption, and effective air pollution control measures, collectively contributed to the synchronous improvement of the economy and air quality in Lhasa. Such "Lhasa pattern" may serve as a positive example for other regional hub cities in China and beyond that experience socioeconomic development and simultaneously seek to improve air quality.