Air Pollution Prevention Research Articles

Hospital admissions attributable to reduced air pollution due to clean-air policies in China.

The Air Pollution Prevention and Control Action Plan (APPCAP) is considered to be the most stringent air pollution control policy in China implemented since 2013. This policy is a milestone in China to mitigate serious air pollution. However, health benefits attributable to reduced fine-particulate air pollution after the implementation of the APPCAP have not been quantitatively estimated on a PM2.5 constituent-specific and morbidity cause-specific basis. Here we conducted a nationwide case-crossover study based on hospital admission records in 292 Chinese cities during 2013-2017. Compared with 2013, the annual average concentrations of PM2.5 and black carbon (BC) in 2017 decreased by 28.61% and 20.35%, respectively. As a result, the average relative reductions in annual attributable fractions of nine major cause-specific hospital admissions associated with PM2.5 and BC were 30.00% and 21.14%, respectively, among which annual attributable fraction for depression showed the largest reduction. Nationally, cities with higher reductions in PM2.5 and BC were found to have higher absolute reductions in annual hospital admission attributable fractions associated with PM2.5 and BC, and geographic inequality in health benefits still existed. Our study highlights the substantial wide-ranging health benefits of reduced PM2.5 and BC levels following the nationwide implementation of the APPCAP in China.

Efektivitas Kegiatan Uji Emisi Kendaraan Kegiatan Bermotor di Dinas Perhubungan Kota Surabaya dalam Mencegah Pencemaran Udara

Effectiveness refers to the implementation of policies that are structured and can achieve the desired goals. The policy in question covers both the central government and regional governments. In the regional government sector, regional leaders often make their own policies in their government, especially policies to prevent public air pollution. The Surabaya City Government implements policies to prevent air pollution. The air pollution prevention policy is addressed to the Surabaya City Transportation Department. In this case, the Surabaya City Transportation Service is tasked with routinely testing motor vehicle exhaust emissions. This is because motor vehicle exhaust gas is one of the main causes of air pollution. By considering the dangers of motor vehicle exhaust emissions on clean air quality, two emission test methods have been implemented in an effort to prevent air pollution. These two methods are believed to be able to prevent air pollution if carried out in accordance with applicable regulations. This is considered quite effective because many motorized vehicles are required to comply because if they don't, heavy sanctions will be imposed. The main aim of implementing this policy is to maintain air quality in the city of Surabaya, which is a densely populated metropolitan city. This research applies an observational approach, with a field survey of the Transportation Service's emission testing activities in the Supervision and Control Sector.

Environmental Regulation and Granting of Land-Use Rights

This study examines the effect of unfunded national environmental regulations on local governments’ land value capture. Exploiting the substantial variation in the regulatory intensity of China's Air Pollution Prevention and Control Action Plan implemented in 2013, we find that environmental regulation increases the growth of land granting fees for prefectural governments. Environmental regulation reduces the local general public budget revenue by shrinking the tax base while increasing the local general public budget expenditure. The post-2013 increase in the net income from land granting could fille more than half of the general public budget gap. Environmental regulation also boosts real estate market development and urbanization through its effect on land granting. We discuss local responses to the unfunded environmental regulation over an extended historical period and conclude that the granting of land use rights significantly enhances the flexibility of legal local government revenue, thereby improving China's fiscal system.

Spatiotemporal evolution of PM2.5 and its components and drivers in China, 2000-2023: effects of air pollution prevention and control actions in China.

This study evaluated the Air Pollution Prevention and Control Action Plan (APPCAP) in China using 2000-2023 data. The average annual PM2.5 concentration dropped from 46.11 ± 16.18µg/m3 to 31.75 ± 14.22µg/m3 (P < 0.05) after APPCAP, with components showing a similar decline. Temporal analysis via Mann-Kendall test indicated a decreasing trend (Z < 0, P < 0.05), seasonally peaking in winter and lowest in summer. Spatially, APPCAP reduced concentration distribution, with key regions improving but areas like Shandong and Henan still facing severe pollution. The main PM2.5 driver shifted from human (e.g., population density) to meteorological (e.g., temperature) factors post-APPCAP, and anthropogenic influence varied across regions. In summary, APPCAP has curbed PM2.5 pollution, yet SO42-, NO3-, and NH4+ remain relatively high, and the increasing human impact in central and southeastern China demands attention in future policies.

Spatial Effect of the New-type Urbanization in the Beijing-Tianjin-Hebei Region and Its Surrounding Areas on the Carbon Emission Intensity

Given that the Beijing-Tianjin-Hebei Region and its surrounding areas are one of the key areas for air pollution prevention and control in China, testing the impact of air quality on the relationship between new-type urbanization and carbon emission intensity is related to the sustainable development of the region. Based on the panel data of the Beijing-Tianjin-Hebei Region and the surrounding "2+26" cities from 2009 to 2021, the nesting weight matrix of air quality distance was constructed, and the internal relationship was tested with the help of the spatial Dubin model and the threshold effect model. The research results showed that： ① The new-type urbanization in this region had a strong inhibitory effect on carbon emission intensity. ② The new-type urbanization development had a significant positive space spillover effect on the carbon emission intensity in the adjacent areas. The air quality distance was the main factor affecting space overflow, but the influence degree was less than the adjacent distance. ③ The inhibitory effect of the new urbanization on carbon emission intensity and air quality was the threshold variable. Based on this, suggestions were put forward to promote the coordinated development of new-type urbanization and carbon emission reduction, strengthen cross-regional coordination of cities, and strengthen the management of air quality standards.

Characterization and Source Apportionment Analysis of PM2.5 and Ozone Pollution over Fenwei Plain, China: Insights from PM2.5 Component and VOC Observations.

PM2.5 and volatile organic compounds (VOCs) have been identified as the primary air pollutants affecting the Fenwei Plain (FWP), necessitating urgent measures to improve its air quality. To gain a deeper understanding of the formation mechanisms of these pollutants, this study employed various methods such as HYSPLIT, PCT, and PMF for analysis. Our results indicate that the FWP is primarily impacted by PM2.5 from the southern Shaanxi air mass and the northwestern air mass during winter. In contrast, during summer, it is mainly influenced by O3 originating from the southern air mass. Specifically, high-pressure fronts are the dominant weather pattern affecting PM2.5 pollution in the FWP, while high-pressure backs predominately O3 pollution. Regarding the sources of PM2.5, secondary nitrates, vehicle exhausts, and secondary sulfates are major contributors. As for volatile organic compounds, liquefied petroleum gas sources, vehicle exhausts, solvent usage, and industrial emissions are the primary sources. This study holds crucial scientific significance in enhancing the regional joint prevention and control mechanism for PM2.5 and O3 pollution, and it provides scientific support for formulating effective strategies for air pollution prevention and control.

Spatial correlation and urban collaborative governance of air pollution in Anhui province based on complex networks

Spatial correlation and urban collaborative governance of air pollution in Anhui province based on complex networks

Quantitative Analysis of Multi-Elements in a Micron-Sized Single Particle Based on Laser-Induced Breakdown Spectroscopy Signal Enhancement of an Optical Fiber Collimated System.

With rapid, energy-intensive, and coal-fueled economic growth, global air quality is deteriorating, and particulate matter pollution has emerged as one of the major public health problems worldwide. It is extremely urgent to achieve carbon emission reduction and air pollution prevention and control, aiming at the common problem of weak and unstable signals of characteristic elements in the application of laser-induced breakdown spectroscopy (LIBS) technology for trace element detection. In this study, the influence of the optical fiber collimation signal enhancement method on the LIBS signal was explored. Then, the influence of the LIBS signal enhancement system based on an optical fiber collimated system on LIBS spectral signal intensity and signal-to-noise ratio (SNR) was compared, and the influences of different spectral preprocessing methods and different variable selection methods on the prediction performance of the random forest (RF) calibration model were investigated. Finally, the Savitzky-Golay convolution derivative (SG)-variable importance projection (VIP)-mutual information (MI)-RF (Zn), first-order derivative (D1st)-variable importance measurement (VIM)-successive projections algorithm (SPA)-RF (Cu), and D1st-VIM-MI-RF (Ni) optimal models were constructed according to the optimal spectral preprocessing method and the optimal hybrid variable selection method. The prediction performances of their optimal RF model after SG-VIP-MI (Zn), D1st-VIM-SPA (Cu), and D1st-VIM-MI (Ni) spectral preprocessing and hybrid variable selection method are presented as follows: Zn (Rp2 = 0.9860; MREP = 0.0590), Cu (Rp2 = 0.9817; MREP = 0.0405), and Ni (Rp2 = 0.9856; MREP = 0.0875). The above results demonstrate that the RF calibration model based on the optical fiber collimated LIBS signal enhancement method, the optimal spectral preprocessing method, and variable selection strategy overcome the key problems of low SNR and low quantitative accuracy in single particle detection. It is expected to provide a theoretical basis and technical support for in situ online rapid monitoring of particulate matter.

A Review of Air Pollution Research in Panzhihua City from 2018 to 2023

Air pollution is a critical issue of concern to society, and researches on this topic has notably increased in recent years. Panzhihua City, a key resource-consuming and heavy industrial city in the southwestern region of China, places air pollution prevention and control at the top of its priorities. However, there is currently a lack of comprehensive reviews on air pollution studies specific to Panzhihua. This paper reviews the research conducted on air pollution and its control methods in Panzhihua City between 2018 and 2023. Ten relevant studies were included, the pollution levels of heavy metals and mineral particles in atmospheric dustfall, PM2.5, PM10, SO2, CO, NO2, and O3 were summarized. The paper also outlined the trends, potential causes of air pollution in Panzhihua, and suggested control measures, aiming to provide a reference for future research and offer a scientific basis for local government policy-making regarding air pollution control

Does urban-rural integration contribute to environmental health? Exploring the interplay between urban-rural integration and air quality dynamics in Yangtze River middle reaches city cluster.

Exploring the coordinated relationship between urban-rural integration and air quality has significant implications for promoting urban-rural development, preventing air pollution and ensuring residents' health. This study takes Yangtze River middle reaches city cluster as a case study, calculates the levels of urban-rural integration and air quality development, analyzes their coupled coordination relationship and driving factors, and explores the path of coordinated development. This study constructs a coupling coordination degree model to analyze the relationship between the urban-rural integration development level and air quality development level. We use the trend surface method to analyze the spatial divergence characteristics of the coordination degree between urban-rural integration and air quality. In addition, we used a geographic detector to analyze the factors affecting the coordination degree. (1) The overall level of urban-rural integration development showed an upward trend. High-value regions were concentrated in the Wuhan, Chang-Zhu-Tan, and Nanchang metropolitan areas. (2) The Air Quality Index showed an overall decline, with the most significant improvements observed in Wuhan, Changsha, and Jiujiang. (3) The coupling degree increased from 0.570 in 2013 to 0.794 in 2021, and the coordination degree increased from 0.337 in 2013 to 0.591 in 2021. The link between urban-rural integration and air quality has deepened over time, and the two promote each other, making city cluster develop towards environmental friendliness. The spatial distribution of coordination degree shows a "high in the west and low in the east, high in the north and low in the south" trend. (4) Per capita GDP, non-agricultural employment ratio, urban-rural spatial circulation media, population urbanization level, and fixed asset investment were identified as the core factors driving the coordination degree between urban-rural integration and air quality. This study confirms that the urban-rural integration and air quality of Yangtze River middle reaches city cluster are gradually changing in the direction of high-quality coordination. However, there are great differences among cities, regional imbalance is prominent, and coordination degree is driven by multidimensional factors.

The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China.

The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China.

Air Pollution and Cognitive Impairment Among the Chinese Elderly Population: An Analysis of the Chinese Longitudinal Healthy Longevity Survey (CLHLS).

Cognitive impairment and dementia have long been recognized as growing public health threats. Studies have found that air pollution is a potential risk factor for dementia, but the literature remains inconclusive. This study aimed to evaluate the association between three major air pollutants (i.e., PM2.5, O3, and NO2) and cognitive impairment among the Chinese elderly population. Study participants were selected from the Chinese Longitudinal Health Longevity Survey (CLHLS) after 2005. We define cognitive impairment as a Chinese Mini-Mental-State Exam (CMMSE) score <24. The associations of air pollution with cognitive impairment and CMMSE score were evaluated with a logistic regression model and a linear mixed-effect model with random intercepts, respectively. A total of 3,887 participants were enrolled in this study. Of the 2,882 participants who completed at least one follow-up visit, 931 eventually developed cognitive impairment. In single-pollutant models, we found that yearly average PM2.5 and NO2 as well as warm season O3, were positively associated with cognitive impairment. NO2 remained positively associated with cognitive impairment in the multi-pollutant model. The linear mixed-effect models revealed that warm season O3 and yearly average NO2 were significantly associated with decreased CMMSE scores. Our research has established a positive association between cognitive impairment and air pollution in China. These findings underscore the imperative for the next iteration of China's Air Pollution Prevention and Control Action Plan to broaden its focus to encompass gaseous air pollutants since mitigating single air pollutant is insufficient to protect the aging population.

A Review of Multi-Objective Optimization Methods of Grid-Connected Hybrid Renewable Energy Systems Combined with Electrical Vehicle Technique

Renewable Energy Sources (RESs) are regarded as highly promising and rapidly advancing forms of renewable energy. The commonly used RESs are solar energy and wind energy. However, obstacles are found in designing RESs. Using vehicle-to-grid (V2G) technology in combination with electric vehicles (EVs) and RESs in smart grid are of great significance for ensuring energy security, preventing air pollution, and promoting energy saving and emission reduction. Over the past decade, V2G technology has enabled EVs to become a potential energy storage capacity for alleviating the random fluctuation in renewable energy generation. Nevertheless, the primary drawbacks of these systems are inefficient energy conversion and substantial initial investment. The sizing of each piece of equipment in the hybrid renewable energy system (HRES) is challenging. Hence, it is imperative to employ a precise sizing technique to determine an ideal arrangement and meet the requisite load requirements. Hence, before the installation of RESs, it is crucial to consider the types and arrangements of photovoltaic (PV) panels and wind turbines (WTs), mathematical models of PV modules and WTs, storage battery options, environmental-economic-technical considerations, sizing methods based on techno-economic objectives, and the ultimate selection of the most optimal configuration. This work lists the general classification of optimization formulation framework, and multi-objective optimization methods of HRESs integrated with electrical vehicle technique are reviewed. This workprovided a thorough examination of the current advancements in the design optimization of HRESs using multi-objectiveoptimization (MOO). This study aims to select the most appropriate design before installing HRESs and provides afoundational platform for scholars interested in exploring the integration of RESs with EVs for further advancement.

REVIEW OF ISLAMIC LAW ON THE IMPLEMENTATION OF LAW NUMBER 32 OF 2009 CONCERNING PROTECTION AND ENVIRONMENTAL MANAGEMENT IN MA'HAD AL-ZAYTUN

This study examines the Islamic legal perspective on Indonesia's Law No. 32 of 2009 regarding environmental protection and management, with a specific focus on Ma’had Al-Zaytun. The research employs a qualitative method with a normative-empirical approach, collecting data through interviews, observations, and documentation. The results show that Ma’had Al-Zaytun implements comprehensive environmental management efforts, focusing on sectors such as livestock, fisheries, and agriculture, alongside environmental health practices. These efforts include the use of liquid and solid waste, pesticide control, soil conservation, and air pollution prevention. The study finds that the implementation of environmental management at Ma’had Al-Zaytun adheres to both Islamic law and national regulations, aligning with the principles of environmental fiqh (jurisprudence). The core of this approach is mashlahah (the common good), which upholds the objectives of Maqasid al-Shariah, focusing on the protection of five essential needs: religion (hifz al-Din), life (hifz al-Nafs), intellect (hifz al-Aql), lineage (hifz al-Nasl), and property (hifz al-Mal). The study highlights the importance of community participation and effective government policies in addressing the increasingly complex environmental challenges.

A Data-Driven Approach to Air Pollution Management with Self-Tuning Deep Regression Neural Network

Air Qualities (AQ) are developed to be a serious environmental and health concern as a result of worldwide population growth. A predictive model for air quality is essential for the timely prevention and management of air pollution. This study proposes a novel Self Tuning Deep Regression Neural Network (STDRNN) for identifying air pollution in the environment using deep neural network. The Air Quality Index (AQI) dataset is employed as the primary source of input data. The methodology begins with a data pre processing phase where the input data finds the missing data and shows the duplicate AQ values using data imputation and remove outlier method. This is followed by feature construction where the each AQ label is converted into an integer AQ value and scaling the features to a similar range using feature encoding and feature scaling method. The AQI data are then given to the STDRNN model for training and testing, enabling accurate prediction. The performance of the proposed model is evaluated using standard metrics such as RMSE, MSE and R2 coefficient. Using the python software AQI dataset shows the comparative analysis with existing methods on similar datasets demonstrates the superiority of the proposed approach in terms of selection performance. This work highlights the potential of STDRNN in advancing the accuracy of the model as well as its promising potential applications.

Short-term effects of air pollutants on hospitalization for childhood respiratory diseases in Suzhou City: a time-stratified case-crossover study

BackgroundShort-term exposure to air pollution has been demonstrated in previous studies to correlate with respiratory disease (RD) in children. Due to regional heterogeneity, our objective was to explore the correlation between short-term exposure to ambient air pollution and hospital admissions for respiratory ailments in children in Suzhou City from January 1, 2017, to December 31, 2022, alongside assessing the influence of the COVID-19 pandemic on this relationship.MethodsWe collected data on air pollutant levels and hospital admissions for childhood respiratory disease (RD) in Suzhou, China, from 2017 to 2022. We utilized a time-stratified case-crossover design along with a conditional logistic regression model to assess the short-term impacts of air pollutants on RD in children through stratified analysis and sensitivity analysis.ResultsA total of 13,408 children with respiratory diseases were included in the study. The findings revealed significant associations between hospitalization for respiratory diseases in children and exposure to PM2.5, PM10, SO2, NO2, and CO. The maximum effect values (95%CI, best lag days) for each 10 µg/m3 increase in the concentrations of PM2.5, PM10, SO2, and NO2 were as follows: 1.017 (1.003–1.031, lag0-2), 1.015 (1.004–1.026, lag0-2), 1.117 (1.001–1.247, lag0-1), and 1.036 (1.009–1.064, lag0-7). Additionally, the maximum effect value (95%CI, best lag days) for each 1 mg/m3 increase in CO concentration was found to be 1.267 1.017–1.579, lag0-7). Stratified analysis indicated that sex, season of admission, and stage of admission did not modify these correlations significantly; however, differential effects on various age groups and sexes were primarily observed among school-age and older children as well as boys.ConclusionsThe short-term exposure to PM2.5, PM10, SO2, NO2, and CO in Suzhou, China, exhibited a positive correlation with RD hospitalization. Prior to the COVID-19 pandemic, the adverse impacts of air pollutants on hospitalizations for childhood respiratory disease were mitigated compared to the period following the pandemic. Local governments should continue promoting decisions and measures for air pollution prevention and control to reduce further pollutant concentration, which is crucial for public health in reducing the burden of childhood respiratory diseases.

Integrated analysis of air quality-vegetation-health effects of near-future air pollution control strategies

Integrated analysis of air quality-vegetation-health effects of near-future air pollution control strategies

Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m3. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control.

Chemical composition, multiple sources, and health risks of PM2.5: A case study in Linyi, China's plate and logistics capital

Chemical composition, multiple sources, and health risks of PM2.5: A case study in Linyi, China's plate and logistics capital

Improving PM2.5 and PM10 predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet

Improving PM2.5 and PM10 predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet