Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses.

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O3, CO, NO2, SO2, 2.5 µm particulate matter (PM2.5), and inhalable particles (PM10)-using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Impact of extreme high temperatures on pollution emissions of enterprise: Evidence from China.

Frequently occurring extreme weather events can pose a challenge to people and production systems. Coping with extreme high temperatures requires promoting the synergy between pollution reduction and carbon reduction. Accordingly, this study examines the causal relationship between extreme high temperatures and corporate pollution emissions by using the panel data of a Chinese sample from 2000 to 2014. This study uses fixed-effects models for the analysis. Baseline results show that a unit increase in the standardized temperature will result in a 4.6% reduction in corporate pollutant emissions. The heterogeneous analysis shows that extreme high temperatures will have an obvious effect on enterprises with low financing constraints and high policy and public constraints as well as on enterprises in cities with a high level of economic development, in innovative cities, and in the eastern region. We also explore the mechanism through which extreme high temperatures reduce pollutant emissions from the two dimensions of external environmental pressure and internal environmental governance. Extreme high temperatures will prompt enterprises to improve their energy efficiency, engage in innovative production processes, adopt source-and-end governance measures, and curb their pollutant emissions while strengthening government environmental supervision. This study provides new ideas for enterprise pollution reduction and serves as an inspiration to the government in formulating environmental policies.

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China.

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Assessing the performance of industrial water resource systems in China with total constraints on water consumption and pollution emissions.

To promote sustainable water resource management, the Chinese government has enacted restrictive policies and targets in terms of total water usage, water intensity, and pollution emissions. While data envelopment analysis (DEA) has been extensively adopted in assessing industrial water resource systems, previous studies have not integrated the realistic constraints on total water consumption and total pollution emissions into a unified framework. This paper examines the system as a two-stage process of water use (WU) and water pollution abatement (WPA), where fixed-sum constraints are imposed on both water consumption and pollution emissions. To address such two-stage DMUs with fixed-sum inputs and fixed-sum outputs, we propose a novel two-stage fixed-sum DEA approach and apply it to evaluate the performance of industrial WU-WPA systems for 29 provinces in China from 2014 to 2018. The results are as follows. From the overall efficiency perspective, the industrial WU-WPA system in China is overall efficient, but provincial overall efficiency polarizes with 19 provinces evaluated as overall inefficient. From a time perspective, overall efficiency shows an increasing then decreasing or descending trend nationally and across the 16 provinces; on the national average, WPA efficiency shows an increasing and then decreasing trend, while WU efficiency demonstrates consistent improvement over time and surpasses WPA efficiency after 2016. In terms of the four areas, regional disparities in the overall efficiencies are converging; for both the whole system and the sub-stage, the eastern area performs the best, followed by the western, northeastern, and central areas. Based on the empirical results, suggestions for improving industrial water resource management are given at the national, regional, and provincial perspectives respectively.

The impact of green finance on industrial pollution emissions: Empirical evidence from economic and environmental perspectives.

Green finance can make full use of financial instruments to control industrial pollution emissions, thus becoming an important initiative to balance ecological environment and economic growth. Based on panel data from 30 Chinese provinces, this study adopts a fixed effect model to test the impact of green finance on industrial pollution emissions, followed by the application of instrumental variables, a GMM dynamic panel, and exogenous shock tests to ensure the robustness of the benchmark results. The results indicate that green finance is capable of controlling the total amount and intensity of industrial pollution emissions, that is to say, to realize the "double control" of industrial pollution emissions, taking into account economic growth and environmental protection. The mediating effect model concludes that green finance can influence industrial pollution emissions through green technology innovation and industrial structure upgrading, but the impact of these two mechanisms on the total amount and intensity of industrial pollution emissions has its own focus. Heterogeneity analysis shows that green finance is more significant in reducing the intensity of industrial pollution emissions in resource-general areas and areas with high levels of information technology, and the shift from controlling the total amount indicator to the intensity indicator implies that green finance is more effective in promoting economic growth while protecting the environment. Our empirical findings offer important policy implications for reducing industrial pollution emissions from both economic and environmental perspectives.

The impact of artificial intelligence on pollution emission intensity-evidence from China.

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

Optimal Environmental Policy for Heterogeneous Governments in China.

The purpose of the article is to study how the shift in the developing philosophy of China's central leadership has impacted the management style of China's local governments and, in turn, the country's economic and environmental equilibrium. We use a real business cycle model with environmental variables and divide governments into those with/without environmental concerns and into those with long- and short-term policy horizons. We find that forcing local governments to plan in the long run is effective only when those governments are simultaneously mandated to consider the environment to be as important as the economy. Theoretical results show that both output and pollution levels are highest under governments without environmental obligations, intermediate under long-term governments with environmental obligations, and lowest under short-term governments with such obligations.

Multi-Tasking Policy Coordination and Corporate Environmental Performance: Evidence from China.

This paper investigates how local governments coordinate the relationship between economic growth targets (EGT) and environmental protection targets (EPT) and the impact of such coordination on firm's environmental performance. Using the pollution emission data of China's industrial firms covering 2003 to 2013, we show that firms in the cities where officials are setting overweighted economic growth targets have more sulfur dioxide intensity, while the central government's hard constraints on EPT included in the official performance evaluation system could partially mitigate the environmental externality of the economic growth target. Further, we find that overweighted EGT significantly decreases firms' desulfurization facilities, capacity, and ratio, while the hard constraint of EPT helps mitigate this negative relationship. We also find that the positive relationship between overweight EGT and firm emissions is more pronounced in the dirty industry, while the hard constraint of EPT helps to mitigate this relationship. The above results help to identify an underlying mechanism of environmental regulation. Finally, we show that converting the hard constraints of environmental protection targets to self-constraint by local government officials could reverse the environmental externality of the economic growth target.

Emission reduction effects of vertical environmental regulation: Capacity transfer or energy intensity reduction? Evidence from a quasi-natural experiment in China.

There is insufficient research on how to reduce the destructive effects of command-based environmental regulation through institutional design. The implementation of the National Key Monitoring Enterprises provides new evidence to assess the effects of vertical monitoring. This study integrates and matches three types of micro databases in China: industrial, pollution, and patent, and constructs firm-level panel data from 2004 to 2010. The empirical evidence shows that the policy reduces the energy use intensity of monitored enterprises by about 10.4% and sulfur dioxide emission intensity by about 23.9%. The mechanism test shows that this effect is achieved by means of energy structure optimization, process innovation, and end-of-pipe treatment, but the effect on total factor productivity is not significant. Among them, the positive impact is stronger for high-profit and emerging firms. Further, we quantify the policy-induced capacity transfer and technology spillovers from monitored enterprises to non-monitored enterprises. In terms of scale, the policy leads to a simultaneous increase in output and pollution emissions of unmonitored firms in the same industry. However, in terms of efficiency, the policy reduces the energy use intensity and pollution emission intensity of enterprises in the same industry.

The Effect of Environmental Policy Uncertainty on Enterprises' Pollution Emissions: Evidence from Chinese Industrial Enterprise.

In response to the global call for emission reduction, China has assumed international responsibility for energy conservation and emission reduction by enacting several environmental policies to save energy and reduce consumption. However, it is debatable whether the increased uncertainty in environmental policies negatively affects firms' emission reduction. Few studies have examined this relationship based on micro-level data. Therefore, this study constructs a theoretical framework of environmental policy uncertainty affecting firms' pollution emissions. Based on comprehensive data from the Chinese Industrial Enterprise Database, the Chinese Industrial Enterprise Pollution Emission Database, and the Chinese Patent Database from 2002 to 2014, we empirically analyzed the impact of environmental policy uncertainty on firms' pollution emissions. The results show that (1) environmental policy uncertainty significantly aggravates the pollution emission intensity of industrial enterprises; (2) environmental policy uncertainty inhibits the improvement of enterprises' innovation capacity, reduces their human capital stock and foreign investment, and aggravates their pollution emission; (3) environmental policy uncertainty has significant heterogeneity on enterprise pollution emissions, that is, environmental policy uncertainty has a greater impact on non-export enterprises, large enterprises, young enterprises, capital-intensive enterprises, state-owned enterprises, and enterprises in polluting industries and central regions. This study provides a useful reference for the improvement of environmental policy and the green transformation of enterprises.

Urgency of technology and equipment upgrades in e-waste dismantling base: Pollution identification and emission reduction.

Recycling of electronic waste (e-waste) and inevitable pollution under current technology have always been a concern of people. Generation and release of pollutants in the recycling process of e-waste are closely related to processing technology and equipment. In this paper, the pollution characteristics of different functional areas and critical processing units in formal e-waste dismantling base have been studied systematically and comprehensively. The results showed that the overall pollutants concentration in crushing workshop and cathode ray tube (CRT) monitor disposing workshop are much higher than other functional areas. Screen-cone glass separation for CRT monitor was the processing unit with the greatest exposure risk and the hazard index (HI) of Pb was 4.60. Pollutant emission factor of the main processing units was calculated and the waste printed circuit board (WPCB) crushing was the most polluted unit. Appropriate improvements in technology and equipment can effectively reduce the generation and release of pollutants. Some reasonable prospects about intelligent equipment and special technologies were proposed for e-waste disposal. All the results provided theoretical and data support for pollution control and technology upgrade of the formal e-waste dismantling base.

Dynamic pollution emission prediction method of a combined heat and power system based on the hybrid CNN-LSTM model and attention mechanism.

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

Association between water and sanitation, air and emission pollution and climate change and neurological disease distribution: A study based on GBD data.

Along with the urbanization and industrialization of countries, the prevalence of chronic diseases has increased. There is ample evidence that ambient pollution can play a major role in these diseases. This study aimed to investigate the association between neurological disorders (NDs) and their subtypes with environmental factors. In this country-level study, we used the age-standardized prevalence and incidence rate (per 100,000 populations) of NDs and its subtypes that have been taken from the Global Burden of Disease (GBD) database in 2019. We used correlation and regression analysis to assess the association between variables. Also, multivariable regression analysis was performed to identify the most important variables in NDs distribution. Age-adjusted NDs incidence rate was significantly higher in developed countries compared to developing countries (11345.25 (95% CI: 11634.88-11055.62) and 9956.37 (95% CI: 10138.66-9774.08)). Association results indicated that the impact of water and sanitation could be more effective than air pollution on NDs. The increase in water and sanitation index levels was positively correlated with NDs incidence rate and prevalence (regression coefficient (b) = 38.011 (SE = 6.50) and b = 118.84 (SE = 20.64), p < 0.001, respectively) after adjusting socio-economic and demographic factors. Furthermore, the incidence of NDs was negatively correlated with the increase in air quality (b = -16.30 (SE = 7.25), p = 0.008). Water and sanitation and their related factors are plausible factors in the distribution of NDs, which may be linked to the potential role of air and water pollution, such as heavy metals and particle matters. These results can be used by politicians and municipal service planners for future planning.

Government intervention, market development, and pollution emission efficiency: Evidence from China.

The government and the market are the two main means for resource allocation, and both play important roles in economic development and environmental protection. Based on the theoretical mechanism analysis, this study empirically investigated the relationship between government intervention, market development, and China's provincial pollution emission efficiency by using the static panel OLS, system generalized method of moments (SYS-GMM), and panel threshold effect model during the period 2000-2017. The results show that the impact of government intervention on China's provincial pollution emission efficiency shows a non-linear U-shaped curve relationship, and there is a positive correlation between market development and provincial pollution emission efficiency in China. Government intervention and market development are complementary, rather than a substitute for each other, in promoting China's provincial pollution emission efficiency. When government intervention is set as the threshold variable, the impact of government intervention on China's provincial pollution emission efficiency shows the feature of "promotes first, then inhibits." However, when market development is set as the threshold variable, government intervention is only conducive to the improvement of China's provincial pollution emission efficiency at a moderate marketization level. Lastly, some policy implications related to the government and the market in enhancing China's provincial pollution emission efficiency are presented.

Thermal degradation and pollutant emission from waste printed circuit boards mounted with electronic components.

Waste printed circuit boards mounted with electronic components (WPCB-ECs) are generated from electronic waste dismantling and recycling process. Air-borne pollutants, including particulate matter (PM) and volatile organic compounds (VOCs), can be released during thermal treatment of WPCB-CEs. In this study, organic substances from WPCB-ECs were pyrolyzed by both thermo-gravimetric analysis (TGA) and in a quartz tube furnace. We discovered that board resin and solder coating were degraded in a one-stage process, whereas capacitor scarfskin and wire jacket had two degradation stages. Debromination of brominated flame retardants occurred, and HBr and phenol were the main products during TGA processing of board resin. Dehydrochlorination occurred, and HCl, benzene and toluene were detected during the pyrolysis of capacitor scarfskin. Benzene formation was found only in the first degradation stage (272-372 °C), while toluene was formed both in the two degradation stages. PM with bimodal mass size distributions at diameters of 0.45-0.5 and 4-5 µm were emitted during heating WPCB-ECs. The PM number concentrations were highest in the size ranges of 0.3-0.35 µm and 1.6-2 µm. The research produced new data on pollutant emissions during thermal treatment of WPCB-ECs, and information on strategies to prevent toxic exposures that compromise the health of recyclers.

Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach.

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids - TSS, total phosphorus - TP and total nitrogen - TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less 'traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling.

Evaluation of the Association between the Risk of Central Retinal Artery Occlusion and the Concentration of Environmental Air Pollutants.

The purpose of the retrospective, population-based study was to assess the relationship between the risk of central retinal artery occlusion (CRAO) and the level of air pollutants. This study identified 2.272 cases of newly diagnosed CRAO registered in the Polish National Health Service database. The study authors gathered hourly ambient concentrations of particulate matter-PM 2.5, PM 10, benzene, carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide from pollution monitoring stations. Data on average daily temperature and atmospheric pressure were also obtained. In the statistical analyses, single- and multi-factor Poisson negative binomial regression models were carried out, controlling also for ambient temperature and atmospheric pressure with seasonality set at a level of 4. This study has shown a positive association between CRAO onset and short-term, daily changes in PM 10, NO2, SO2, O3, and CO concentrations, as well as with air temperature, in the days preceding the diagnosis.

Effect of Non-Coal Heating and Traditional Heating on Indoor Environment of Rural Houses in Tianjin.

In order to understand the effect of the non-coal heating and the traditional coal-fired heating on the indoor environment of the rural houses, the humidity environment and indoor air quality in several households were investigated during the heating period in Beichen District and Wuqing District of Tianjin, China. The results showed that the indoor average temperature for the heating by the electricity and the natural gas was higher than that by the traditional coal fire. The indoor relative humidity for the heating by the electricity and the natural gas was lower than that by the traditional coal fire. The indoor air quality (IAQ) for the heating by the electricity and the natural gas was better than that by the traditional coal fire. For traditional coal-fire heating households, the indoor pollutant emission (CO emission) by using the clean coal was lower than that by using the raw coal. The indoor ventilation rate which was an important parameter for the indoor air quality was generally poor in winter. The total volatile organic compounds (TVOC) emission in the indoors of the coal-fired heating households was generally higher than that of the non-coaled heating households.

Oil Price Uncertainty, Transport Fuel Demand and Public Health.

Based on the panel data of 306 cities in China from 2002 to 2012, this paper investigates China's road transport fuel (i.e., gasoline and diesel) demand system by using the Almost Ideal Demand System (AIDS) and the Quadratic AIDS (QUAIDS) models. The results indicate that own-priceelasticitiesfordifferentvehiclecategoriesrangefrom-1.215to-0.459(byAIDS)andfrom -1.399 to-0.369 (by QUAIDS). Then, this study estimates the air pollution emissions (CO, NOx and PM2.5) and public health damages from the road transport sector under different oil price shocks. Compared to the base year 2012, results show that a fuel price rise of 30% can avoid 1,147,270 tonnes of pollution emissions; besides, premature deaths and economic losses decrease by 16,149 cases and 13,817.953 million RMB yuan respectively; while based on the non-linear health effect model, the premature deaths and total economic losses decrease by 15,534 and 13,291.4 million RMB yuan respectively. Our study combines the fuel demand and health evaluation models and is the first attempt to address how oil price changes influence public health through the fuel demand system in China. Given its serious air pollution emission and substantial health damages, this paper provides important insights for policy makers in terms of persistent increasing in fuel consumption and the associated health and economic losses.

[Estimation of Co-benefits from Pollution Emission Reduction by Eliminating Backward Production Capacities in Hainan Province].

Industrial structural adjustment plays a significant role in achieving the pollution reduction goals in China. It is an optimal choice for Hainan Province to promote industrial structure upgrading and build a "national demonstration area for ecological civilization construction". The emission factor method was used to estimate co-benefits of pollution emission reduction through closure of backward production capacities as a result of industrial structural adjustment policies. The results showed that in Hainan Province the cumulative reduction emissions of NOx, dioxins and mercury were 2826.0 t·a-1, 10462.5 mg·a-1 and 280.8 kg·a-1 respectively from 2006 to 2013, taking into consideration of cement, iron & steel, paper making and solid clay bricks sectors. The impact of eliminating backward production capacities on reductions of NOx was not remarkable, but the impacts on control over dioxins and mercury emissions were significant. The paper provided a new approach for estimating co-benefits from reducing the conventional pollutants and emerging pollutants.