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Corona virus diseases-2019 (COVID-19), an infectious disease identified in late December, 2019, in Wuhan city of China, was declared a pandemic by the World Health Organization. Most countries including India have announced some sort of lockdown to reduce the effects of COVID-19 and discontinue the transmission of novel coronavirus. Major negative effects on the social and surrounding environment have been reported due to COVID-19, however positive effects have also been observed with respect to air quality. Secondary results have been taken from National Aeronautics and Space Administration (NASA), indicating significant reduction (50%) in air quality of Indian region. This perspective highlights the effects of the lockdown due to COVID-19 on aerosol optical thickness specifically in India.

Public participation is crucial to improving the performance of water governance systems, especially in a governance model dominated by a top-down hierarchical structure. Public concerns, as a vital form of public participation, have been acknowledged as an essential component in contributing to water governance. However, few studies explore the varying effects of diverse public concerns in leading to different water governance outcomes. This study addresses this gap by exploring the direct and indirect effects of water-related public concerns on water pollution control and governments’ pollution mitigating actions, using citizens’ water-related posts crawled from China’s social media. Results show that public water-related concerns contribute to water governance both through its direct effects on reducing water pollution and indirect effects by promoting policy actions. Specifically, the concerns related to water pollution hazards , water pollution monitoring , prevention products , and water pollution control measures have more positive impacts on water pollution reduction compared to other types of concern. Meanwhile, public concerns demonstrate stronger effects in triggering economic-related and infrastructure-related water pollution mitigation actions. This study provides nuanced insights to understand the role of public participation in improving water governance, the findings of which are insightful to enhance accountability of water governance systems through a bottom-up approach.

The reform of environmental management systems is key to improving environmental pollution treatment and green technology innovation. Based on panel data on 30 provincial administrative regions in China for 2008 to 2016, this paper analyzes the impacts of environmental decentralization and environmental protection investment on green technology innovation. It is first found that environmental decentralization promotes green technology innovation after inhibition. Similar effects are found for environmental administrative decentralization, environmental monitoring decentralization, and environmental supervision decentralization. Second, in the long run, environmental decentralization in developed and low-emission regions is more conducive to green technology innovation. Third, environmental pollution treatment investment has a significant inhibiting effect on green technology innovation under high levels of environmental decentralization, and the inhibiting effects of industrial pollution source treatment investment and “three simultaneous” construction project investments are particularly obvious. This paper explores green technology innovation as the goal of environmental decentralization, which is the driving force behind pollution control. From the perspective of environmental protection investment, the paper further analyzes the impact of environmental decentralization on green technology innovation. The study has important reference value for determining reasonable levels of environmental decentralization among different levels of governance and in formulating differentiated strategies of environmental decentralization.

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

The topic of air pollution and its effect on public health has become a hot policy issue that has attracted worldwide attention, but this attention has seldom been extended to the causal relationship between atmospheric environmental policy (AEP), air pollution, and public health. This paper uses panel data from 30 provinces in China to construct spatial econometric models that analyze the impact of AEP on air pollution, the impact of air pollution on public health, and the mediation effect that air pollution may have between AEP and public health. The results demonstrate that there is a significant positive spatial spillover effect of soot and dust (SD) emission intensity and the overall air pollution level as measured by the Air Pollution Index (API). The AEP has significant inhibitory effects on the intensity of sulfur dioxide and SD emissions, as well as on overall air pollution. An increase in the overall air pollution level has a significant detrimental effect on public health as measured by average life expectancy. Air pollution as measured by API is a mediating factor in the relationship between AEP and public health. The study results could help to effectively control air pollution and promote public health by leading to improvements in regional pollution prevention and control mechanisms and strengthening of the central government’s policy formulation and local governments’ policy implementation process.

Agricultural non-point source pollution (ANPSP) is one of the important factors leading to water environmental pollution. Identifying the spatial distribution of ANPSP and implementing regional control measures are, therefore, important for ensuring effective pollution prevention and control. However, analyzing regional ANPSP using a single approach is challenging due to the impacts of geographical, economic, and policy differences. In this context, the present study aims to assess the long-term spatiotemporal characteristics of pollutants and their sources in Henan Province over the 2001-2023 period using inventory analysis, equal standard pollution load method, and cluster analysis. In addition, we investigated the decoupling relationship between ANPSP and agricultural output value using the Tapio decoupling model. The results showed that: (1) distinct variation stages of total pollution, including total emission reduction, structural transition, and emerging conflicts. Specifically, there was a increase in total pollution over the 2001-2006 period, followed by a fluctuation, continuous decrease, and stabilization in the 2007-2013, 2014-2019, and 2020-2023 periods, respectively. The pollution loads of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were reduced by 26.2, 23.5, and 18.2%, respectively. In addition, increases in the contribution rates of livestock and farmland straw. On the other hand, rural households and livestock were the main sources of COD and TP emissions, respectively. The main source of TN emissions has shifted from livestock to farmland straw; (2) the total pollutant load exhibited a distinct spatial distribution pattern. Specifically, the southern part of the study area had the highest pollutant loads, followed, respectively, by the eastern, northern, and western parts; (3) the decoupling relationship between ANPSP emissions and agricultural output values showed fluctuating changes, dominated by weak and strong decoupling status, with gradual improvement. (4) Henan Province was divided into three primary non-point source pollution control zones using cluster analysis, namely high, moderate, and low-risk zones. The high, moderate, and low risk areas had average equivalent pollution indices of 61.89, 40.44, and 15.37, respectively. In this study, we proposed targeted prevention and control measures for ANPSP in Henan Province. These findings provide a reference for the governance and planning of ANPSP in Henan Province, as well as a novel perspective for investigating the relationship between rural development and the environment.

There is an urgent need to explore the current situation of China’s water pollution incidents for policymaking, accident prevention, and risk mitigation. This study analyzed 1528 water pollution incidents in China from 2001 to 2020 and explored the spatiotemporal characteristics and causes of incidents and consequent damage. The frequency of water pollution accidents increased in 2004, peaked in 2006, and decreased thereafter with a significant decline in 2016. Due to the developed river networks, high population densities, and increasing environmental awareness, pollution incidents were mostly concentrated in China’s relatively industrially developed eastern coastal regions. Illegal emission is the major cause, accounting for 51% of all incidents, but with pipeline construction gradually approaching a peak, pipeline leakage poses a noteworthy risk in the future. Although the severity of accidents has reduced recently, it is still necessary to strengthen the risk prevention strategies for general and major accidents. Furthermore, three key factors, including economic development, regulations and legal system, and public participation in pollution supervision and control, which affect the trends and characteristics of water pollution incidents in China, were discussed. This paper offers valuable insights and suggestions that may have useful implications for policymaking and the prevention of water pollution.

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson’s correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH 3 –N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Investment in the new energy industry (IEI) and industrial pollution prevention (IPP) is an emerging strategy for carbon neutrality. However, little attention has been paid to exploring its direct linkage with environmental degradation. Therefore, the present study is an effort to contribute to a thin body of literature by recruiting IPP and IEI in China’s provincial-level carbon emissions (CO 2 ). With the use of the latest maximum data from 1998 to 2017, the long-run relationships are estimated through “augmented mean group estimator (AMG), panel fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), and Pairwise Dumitrescu Hurlin (DH) Panel Causality Tests.” According to the above-stated econometric techniques, IEI and IPP negatively affect CO 2 emissions, which means the reduction in environmental degradation. The study also validates the environmental Kuznets curve (EKC) hypothesis for 30 provinces of China. Additionally, IPP shows the long-run unidirectional causal relationship with CO 2 emissions, while income level has a bidirectional causal relationship with CO 2 emissions. Findings have robust policy implications for China’s provinces.