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In China, Hebei Province and Guangdong Province have many pollution-intensive industries; yet Hebei suffers more serious atmospheric or environmental pollution than Guangdong. To explain the phenomenon, this paper chooses the statistical data of 286 prefecture-level cities in the Chinese mainland from 2005 to 2018 and empirically tests the spatial effect and threshold characteristics of atmospheric self-purification capacity on environmental pollution by using the spatial autoregression model (SAR), spatial Durbin model (SDM), and panel threshold model. As evinced, the local atmospheric self-purification capacity has a significant inhibitory effect on environmental pollution, and the absolute value of the direct effect coefficient reaches 1.337. Simultaneously, atmospheric self-purification capacity has a threshold effect on environmental pollution. The threshold value of environmental regulation as a threshold variable is 11.6349. This means that when the level of environmental regulation proves higher than 11.6349, it attenuates the inhibitory effect of the atmospheric self-purification capacity on environmental pollution. As the heterogeneity research unveils, atmospheric self-purification capacities in various regions form a significant correlation with environmental pollution. This paper suggests that local governments should strengthen environmental regulation and construct an inter-regional joint prevention and control system for atmospheric-pollution governance to enhance the atmospheric self-purification capacity. [ABSTRACT FROM AUTHOR]

The hand foot, and mouth disease (HFMI)) epidemic has become a serious public health problem worldwide with a high economic and health burden. In addition, the prevalence of (HFMI) varies greatly between cities. Therefore, this paper aims to reveal high-risk cities for HFMD and associated meteorological and air pollution factors in Guangdong Province during 2014-2018. Data on the incidence of HFMD mid meteorological and air pollution. factors were obtained from the Guangdong Provincial Center for Disease Control and Prevention and Guangdong Meteorological Service. Three different grey incidence analysis (GIA) were used, namely Deng's grey incidence analysis, absolute degree of GIA, and second synthetic degree of GIA. Additionally, GMC (1,1) model was used to predict the trend of HFMD in 2019-2028. According to the second synthetic degree of GIA, the top three cities most affected by HFMD are Zhuhai, Guangzhou, and Foshan. However, the prediction model .found that the incidence of HFMD in Guangdong Province will generally decline in the next 10 years, indicating that the prevention and control measures are still relatively in place. Deng's grey incidence analysis found that CO, %O2, PM2.5, PM10), and wind scale are closely related to the incidence of HFMD. It is recommended to closely monitor weather change and urban air quality and take protective measures against HFMD in advance. The results of this study hare substantial implications for the control of HFMD. [ABSTRACT FROM AUTHOR]

With severe haze pollution continuing to frequently occur in many regions of China in recent years, increasing research effort has been made to study the source and control of haze pollution using socio-economic factors. Based on the urban panel data of 21 cities in Guangdong Province, South China, from 2005 to 2016, this paper analyzes the spatial and temporal evolution trend, spatial autocorrelation and influencing factors of Guangdong's haze pollution through spatial econometric methods with inverse distance weight. First, the results show that the haze pollution of 21 cities in Guangdong Province has strong spatial autocorrelation and spatial clustering phenomenon. Aggregation areas of heavy air pollution are located in the Pearl River Delta region centered on Guangzhou, Foshan, Zhongshan and Dongguan. Those cities should be targeted as priority haze-pollution-reduction areas. Secondly, significant spatial dependence and spatial spillover effects of haze pollution in Guangdong are demonstrated by the spatial econometric models. Joint prevention and control of haze pollution between cities are therefore important and necessary. Thirdly, the direct and indirect effects of energy consumption on haze pollution are positive and significant at the 1% level. Meanwhile, population, foreign direct investments (FDI) and the ratio of the secondary industry (e.g., manufacturing and construction industries) relative to the service industry also play important roles in haze pollution, and all of them have positive effects on air pollution. However, the environmental Kuznets curve (EKC) hypothesis is not supported by the economy-emission data of Guangdong. Economic growth and car density are not significant factors in deciding the level of haze pollution. To effectively improve the local air quality, the government is recommending the introduction of policies for adjusting the structures of energy, economy and FDI, promoting the research, development and application of clean technologies, and enhancing the awareness of energy conservation among residents and enterprises. [ABSTRACT FROM AUTHOR]

• A big data computational text analysis based on a corpus of 2.4 million words. • Comparison of major themes on air pollution in stakeholder discourse in Hong Kong. • Three stakeholder groups are studied; all focus on emissions and pollution control. • Public health is a greater concern in MEDIA compared with other groups. • More personalized health information to be shared with the vulnerable groups. Stakeholder engagement is critical to the successful formulation and implementation of environmental policies. Text analysis can serve as an important means to decipher stakeholder (dis)agreements. Traditionally, researchers analyze texts using qualitative text analysis. Relying only on qualitative text analysis can no longer be sufficient to handle very large amounts of textual data. Now computational methods can be applied to help extract opinions in electronic texts. Our paper adopts a big data computational text analysis approach (keyword analysis, keyword co-occurrence, thematic analysis), based on a corpus of 2.4 million words, to compare concerns towards air pollution among three stakeholder groups (i.e. the government (GOV), the environmental groups (NGO) and the news media (MEDIA)) in Hong Kong, between 2002 and 2012, a period when air pollution was subject to rigorous policy debates and discussions. Our analysis shows the somewhat different concerns of the stakeholders. The stakeholders focus heavily on emissions and end-of-pipe pollution control. Though the advocation of sustainability since 1990s, the government-led command-and-control approach still dictates the discourse. Sustainability is featured more intensively by the Hong Kong Special Administrative Region (HKSAR) Government. Transboundary pollution (e.g. from Guangdong, China) is not a major concern for all stakeholder groups. Public health is a greater concern in MEDIA, as compared to the other two stakeholder groups. Future air pollution policy-making may direct more of its attention to the pressing concerns over the negative impacts of air pollution on health and its associated costs, with health experts getting more closely involved in the regulatory decision-makings. More information should be given to vulnerable groups, such as young children or workers in polluted environments. To better relate differential health risks of pollution in everyday life, both local and international governments should consider developing more personalized air pollution monitoring and health management systems for their citizens in future. [ABSTRACT FROM AUTHOR]

The summertime air pollution events endangering public health in the Guangdong–Hong Kong–Macao Greater Bay Area are connected with typhoons. The wind of the typhoon periphery results in poor diffusion conditions and favorable conditions for transboundary air pollution. Random forest models are established to predict typhoon-associated air quality in the area. The correlation coefficients and the root mean square errors in the air quality index (AQI) and PM 2.5 , PM 10 , SO 2 , NO 2 and O 3 concentrations are 0.84 (14.88), 0.86 (10.31 µgm-3), 0.84 (17.03 µgm-3), 0.51 (8.13 µgm-3), 0.80 (13.64 µgm-3) and 0.89 (22.43 µgm-3), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature importance output of the models, the differences in the meteorological drivers of typhoon days and non-typhoon days are revealed. On typhoon days, the air quality is dominated by local source emission and accumulation as the sink of pollutants reduces significantly under stagnant weather, while it is dominated by the transportation and scavenging effect of sea breeze on non-typhoon days. Therefore, our findings suggest that different air pollution control strategies for typhoon days and non-typhoon days should be proposed. [ABSTRACT FROM AUTHOR]

Noncooperative policies for transboundary pollution abatement are inefficient and ineffective, but cooperative policies for pollution abatement are seldom successful in regions that have different development goals and conflicting interests. In this paper, by extracting the key components that lead to the success of pollution reduction policies between Hong Kong (HK) and Guangdong, we aim to construct a general framework for cooperative environmental management. The incentive for and the dynamic trajectory, core measures, and outcomes of cooperative pollution abatement policies to address transboundary air pollution were studied using incrementalism analysis. The context that drives the abnormal increment of cooperative policies between HK and Guangdong was analyzed to determine the factors that affect the full cooperation process. The political union between HK and China is the main driving force for fostering cooperative policies in these two regions that have highly asymmetric economic development levels and pollution emission intensities. The factors for successfully designing cooperative pollution reduction policies are revealed from trust building, political support, efficient governance, precise policies, a rational cost-sharing mechanism, and detailed management and implementation plans. Innovative multi-level and cross-department governance, and an unequal but rational cost-sharing scheme sustain long-term cooperation. A general framework is derived from these empirical key components and constructed as a systematic approach to provide a guide for designing cooperation in transboundary pollution reduction for the sustainable management of cross-border natural resources in different regions with conflicts of interest. • An empirical framework of cooperative air pollution abatement model is constructed. • Factors contributing to foster cooperative polices is analyzed via incrementalism model. • Political support produces adequate institutional arrangements and precise policies. • A matching cost-sharing mechanism can catalyze and stabilize the cooperation model. • Multi-level and cross-department policies sustain long-term cooperation. [ABSTRACT FROM AUTHOR]

As air pollution becomes more and more severe, air quality prediction has become an important approach for air pollution management and prevention. In recent years, a number of methods have been proposed to predict air quality, such as deterministic methods, statistical methods as well as machine learning methods. However, these methods have some limitations. Deterministic methods require expensive computations and specific knowledge for parameter identification, while the forecasting performance of statistical methods is limited due to the linear assumption and the multicollinearity problem. Most of the machine learning methods, on the other hand, cannot capture the time series patterns or learn from the long-term dependencies of air pollutant concentrations. Furthermore, there is a lack of methods that could generate high prediction accuracy for air quality forecasting at larger temporal resolutions, such as daily and weekly or even monthly. This paper, therefore, proposes a deep learning-based method namely transferred bi-directional long short-term memory (TL-BLSTM) model for air quality prediction. The methodology framework utilizes the bi-directional LSTM model to learn from the long-term dependencies of P M 2.5 , and applies transfer learning to transfer the knowledge learned from smaller temporal resolutions to larger temporal resolutions. A case study is conducted in Guangdong, China to test the proposed methodology framework. The performance of the framework is compared with other commonly seen machine learning algorithms, and the results show that the proposed TL-BLSTM model has smaller errors, especially for larger temporal resolutions. • A transfer learning-based bi-directional long short-term memory model is proposed. • BLSTM achieves better prediction performance at smaller temporal granularities. • Transfer learning can lower the error of BLSTM at larger temporal granularities. • Self-transfer learning contributes little to improve, or even worsens the performance. [ABSTRACT FROM AUTHOR]

The Pearl River Delta, after pell-mell industrialization, suffers air pollution. Two-thirds of the pollutants come from Hong Kong-owned factories in Guangdong. Direct 2011 medical costs in Hong Kong are reliably estimated at US$513 million, and annual excess Hong Kong deaths from air pollution exceed 3,000. Dirty air hurts quality of life, recruitment of professionals, tourism, and other desiderata-including government legitimacy. This article surveys the relevant politics, medicine, economics, and air chemistry, recommending cap-and-trade policies that are acceptable where markets now rule in both 'systems' of Hong Kong- Guangdong. It stresses the need for official enforcement of caps. Although local governments have cooperated to establish air-monitoring stations, they have yet to leverage Hong Kong's expertise in regulation and Guangdong's potential for manufacture of air-cleansing equipment. They need a regional emissions trading scheme ( ETS). With Beijing's plans to have a nationwide ETS by 2015, this severe problem could be solved sooner rather than later. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious problem in the Pearl River Delta, South China, particularly in winter due to the local micrometeorology. In this study, atmospheric polycyclic aromatic hydrocarbons (PAHs) were monitored weekly in Shenzhen during the winter of 2006. Results indicated that the detected PAHs were mainly of vapor phase compounds with phenanthrene dominant. The average vapor phase and particle phase PAHs concentration in Shenzhen was 101.3 and 26.7 ng m − 3, respectively. Meteorological conditions showed great effect on PAH concentrations. The higher PAHs concentrations observed during haze episode might result from the accumulation of pollutants under decreased boundary layer, slower wind speed, and long-term dryness conditions. The sources of PAHs in the air were estimated by principal component analysis in combination with diagnostic ratios. Vehicle exhaust was the major PAHs source in Shenzhen, accounting for 50.0% of the total PAHs emissions, whereas coal combustion and solid waste incineration contributed to 29.4% and 20.6% of the total PAHs concentration, respectively. The results clearly indicated that the increasing solid waste incinerators have become a new important PAHs source in this region. [ABSTRACT FROM AUTHOR]