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Pollution havens create environmental inequality issues. China's recent policy of directing high-pollution firms to migrate out of its capital, Beijing, offers a case of a pollution haven that was mandated, rather than resulting from firms' responses to environmental regulation. More importantly, it leads to a question that has been less discussed in previous literature: does a pollution haven strike back? After the highly polluting enterprises moved out of Beijing to neighboring Hebei province, Hebei has been blamed for the continuing poor air quality in Beijing, based on a perception that the pollutants emitted in Hebei are being blown back to Beijing. This paper investigates the existence of a pollution haven in Hebei and quantifies the contribution of Hebei's emissions to Beijing's air quality. We find that the proportion of output values and the relative concentration of high-pollution industries in Hebei increased when the large-scale out-migration began, while the indicators in Beijing decreased. We also find higher emissions of enterprises in Hebei than their counterparts in Beijing. Together, these findings indicate the existence of a "manmade" pollution haven. Further, based on high-frequency spatial data, we combine an Autoregressive Distributed Lag model and a Spatial Autoregressive Model and decompose the factors that affect Beijing's air quality into local and regional transport of air-borne pollutants. We find a significant effect of the neighboring cities' air pollution on Beijing's air quality. This indicates that the pollution haven indeed struck back. These findings imply the importance of regional policies to control air pollution. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Self-reported life satisfaction of China's population has not improved as much as expected during the economic boom, which was accompanied by a significant decline in environmental performance. Is environmental pollution the culprit for the lagging subjective well-being? To explore this issue, this paper adopts sentiment analysis to construct a real-time daily subjective well-being metric at the city level based on the big data of online search traces. Using daily data from 13 Chinese cities centred on Beijing between August 2014 and December 2019, we look at the corelation between subjective well-being and air pollution and the heterogeneity in this relationship based on two separate identification strategies. We find that air pollutants are negatively correlated with subjective well-being, and well-being tends to decline more from pollution during hot seasons. In addition, residents in wealthier regions tend to be more sensitive to air pollution. This result may be explained by the differences in the subjective perception of air pollution and personal preferences at different levels of income. These findings provide information about concerns of the public, thereby helping the government to take appropriate actions to respond to the dynamics of subjective well-being. • Sentiment analysis and big data are adopted to construct a daily city-level subjective well-being metric. • Higher levels of airborne particulates lead to a decline in subjective well-being. • Peoples' well-being tends to decline more from pollution during hot seasons. • Residents in wealthier regions tend to be more sensitive to air pollution. [ABSTRACT FROM AUTHOR]

• A novel deep learning model is proposed for PM 2.5 prediction. • A new correlation calculation method is proposed to construct dynamic adjacency matrices. • Two real-world datasets are selected to verify the performance of models. Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents' travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability. [ABSTRACT FROM AUTHOR]

With a decrease in fine particulate matter (PM 2.5) due to the implementation of air pollution prevention-control measures, ozone (O 3) has risen in China especially in urban areas recently. It becomes more and more important to control air combined pollution with high concentration of PM 2.5 and O 3. We analyzed the characteristics of air combined pollution in Beijing from 2014 to 2019 in this paper. Results indicated that the trough O 3 showed an upward trend of 4.5 μg m−3 yr−1, while the peak decreased by −1.1 μg m−3 yr−1. There were three typical atmospheric chemical stages in the interaction between O 3 and PM 2.5 in summer through observations. The first was an intense photochemical stage with the synchronous and rapid growth of O 3 and PM 2.5. The O 3 concentration varied from 68 μg m−3 to 206 μg m−3 when PM 2.5 ≤ 35 μg m−3. While secondary inorganic ions (SNA) ranged from 2 to 21 μg m−3 especially SO 4 2− (0.8–12 μg m−3). The second was an aerosol chemical generation stage with the rapid increase of PM 2.5 and SNA under high O 3 concentration. O 3 was maintained at a high concentration between 198 μg m−3 and 241 μg m−3 when PM 2.5 was within the range of 35–125 μg m−3. SNA increased from 12 μg m−3 to 96 μg m−3. The third was a high PM 2.5 pollution stage suppressing the generation of O 3 when PM 2.5 ≥ 125 μg m−3. O 3 began to decline with the growth of PM 2.5 and decreased from 224 μg m−3 to 130 μg m−3. Ultraviolet radiation (UV) decreased from 13 MJ m−3 in the first to 8 MJ m−3 in this stage. [Display omitted] • The O 3 trough increased by 4.5 μg/m3 and the peak decreased by −1.1 μg/m3 per year in Beijing. • There were three typical processes in the interaction between O 3 and PM 2.5 in summer day. • The photochemical process was intense in dry-sunny days with the synchronous growth of O 3 and PM 2.5. • Aerosol chemical process intensified with a rapid growth of PM 2.5 and SNA in high O 3. • O 3 began to decline with the growth of PM 2.5 when PM 2.5 attenuated UV in a high level. [ABSTRACT FROM AUTHOR]

China's most critically air polluted area, the Beijing-Tianjin-Hebei (BTH) region has attracted significant attention; the regional linkage of environmental indicators in BTH has been widely studied in recent years. The city-level air pollutants and economic benefits hidden in trade activities are still poorly understood. This paper examines air pollutants and economic benefits embodied in BTH's consumption based on a city-level input-output table. Our results show that transfer flows of air pollutants and value-added between different cities lead to the unequal exchange of air pollution and economic benefits in BTH. Beijing gains more value-added (38.40%) through trade compared with its atmospheric pollutant equivalents (APE, 1.75%) induced by consumption demand in BTH. Conversely, Tangshan, Shijiazhuang, and Handan emit more air pollutants compared with the benefits they gain through trade. The results provide evidence for the establishment of compensation mechanisms between cities. • Trade between cities lead to the unequal exchange of air pollution and economic benefits in BTH. • Beijing gains a higher proportion of value added compared with its air pollutants induced by trade. • Cities in Hebei province gain lower proportions of value added compared with their air pollutants induced by trade. [ABSTRACT FROM AUTHOR]