Health benefit assessment of PM2.5 reduction in Pearl River Delta region of China using a model-monitor data fusion approach

Does environmental regulation lessen health risks? Evidence from Chinese cities

Introduction

Atmospheric pollution is a severe problem confronting the world today, endangering not only natural ecosystem equilibrium but also human life and health. As a result, governments have enacted environmental regulations to minimize pollutant emissions, enhance air quality and protect public health. In this setting, it is critical to explore the health implications of environmental regulation.

Methods

Based on city panel data from 2009 to 2020, the influence of environmental regulatory intensity on health risks in China is examined in this study.

Results

It is discovered that enhanced environmental regulation significantly reduces health risks in cities, with each 1-unit increase in the degree of environmental regulation lowering the total number of local premature deaths from stroke, ischemic heart disease, and lung cancer by approximately 15.4%, a finding that remains true after multiple robustness tests. Furthermore, advances in science and technology are shown to boost the health benefits from environmental regulation. We also discover that inland cities, southern cities, and non-low-carbon pilot cities benefit more from environmental regulation.

Discussion

The results of this research can serve as a theoretical and empirical foundation for comprehending the social welfare consequences of environmental regulation and for guiding environmental regulation decision-making.

Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts

Ambient ozone (O3) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O3 formation processes. The emission-based chemical transport models (CTM) are broadly used to predict O3 formation, but they may deviate from observations due to input uncertainties such as emissions and meteorological data, in addition to the treatment of O3 nonlinear chemistry. In this study, an innovative recurrent spatiotemporal deep-learning (RSDL) method with model-monitor coupled convolutional recurrent neural networks (ConvRNN) has been developed to improve O3 predictions of CTM. The RSDL method was first used to build the ConvRNN within a 24-h scale to characterize the spatiotemporal relationships between the monitored O3 data and CTM simulations, and then incorporated the recurrent pattern to achieve 72-h multi-site forecasts based on a pilot study over the Pearl River Delta (PRD) region of China. The results showed that the RSDL method predicted O3 with high accuracy over this case study, with an increase of 27.54% in the correlation coefficient (R) average for all sites as well as an increase in R of 0.14-0.21 for all cities compared to CTM. Moreover, the regional distribution of CTM was further improved by the RSDL predictions with the data fusion technique, which greatly reduced the underpredictions of O3 concentrations, particularly in high O3-level areas (concentrations >160 μg/m3), with a 33.55% reduction in the mean absolute error (MAE).

Health benefits of reducing ambient levels of fine particulate matter: a mortality impact assessment in Taiwan

While numerous studies have found a relationship between long-term exposure to airborne fine particulate matter (PM2.5) and higher risk of death, few investigations examined the contribution that a reduction of exposure to ambient PM2.5 levels might exert on mortality rates. This study aimed to collect data on changes in annual average ambient levels of PM2.5 from 2006 to 2020 and consequent health impact in public health in 65 municipalities in Taiwan. Avoidable premature mortality was used here as an indicator of adverse health impact or health benefits. Annual PM2.5 levels were averaged for the years 2006, 2010, and 2020. In accordance with World Health Organization (WHO) methodology, differences were estimated in the number of deaths attributed to ambient PM2.5 exposure which were derived from concentration-response data from prior epidemiological studies. PM2.5 concentrations were found to have been decreased markedly throughout Taiwan over the two-decade study. As the PM2.5 concentrations fell, so was the health burden as evidenced by number of deaths concomitantly reduced from 22.4% in 2006 to 8.47% in 2020. Data demonstrated that reducing annual mean levels of PM2.5 to PM10 ug/m3 was associated with decrease in the total burden of mortality, with a 2.22-13.18% fall in estimated number of PM2.5-related deaths between 2006 and 2020. Based upon these results, these declines in ambient PM2.5 levels were correlated with significant improvement in public health (health benefits) and diminished number of deaths in Taiwan.

Chronic and acute health effects of PM2.5 exposure and the basis of pollution control targets

Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.

Impact of commercial cooking on urban PM2.5 and O3 with online data-assisted emission inventory

Commercial cooking (CC) is an intensive near-field source contributing to ambient PM_2.5 and O₃ concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM_2.5 and O₃ were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM_2.5 and O₃ concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m³ of PM_2.5, and accounted for 24±1 % of PM_2.5 concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM_2.5 was predicted in areas with elevated CC emissions, while the contribution to O₃ was insignificant.

The pathophysiological and molecular mechanisms of atmospheric PM2.5 affecting cardiovascular health: A review

BACKGROUND

Exposure to ambient fine particulate matter (PM2.5, with aerodynamic diameter less than 2.5 µm) is a leading environmental risk factor for global cardiovascular health concern.

OBJECTIVE

To provide a roadmap for those new to this field, we reviewed the new insights into the pathophysiological and cellular/molecular mechanisms of PM2.5 responsible for cardiovascular health.

MAIN FINDINGS

PM2.5 is able to disrupt multiple physiological barriers integrity and translocate into the systemic circulation and get access to a range of secondary target organs. An ever-growing body of epidemiological and controlled exposure studies has evidenced a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality. A variety of cellular and molecular biology mechanisms responsible for the detrimental cardiovascular outcomes attributable to PM2.5 exposure have been described, including metabolic activation, oxidative stress, genotoxicity, inflammation, dysregulation of Ca2+ signaling, disturbance of autophagy, and induction of apoptosis, by which PM2.5 exposure impacts the functions and fates of multiple target cells in cardiovascular system or related organs and further alters a series of pathophysiological processes, such as cardiac autonomic nervous system imbalance, increasing blood pressure, metabolic disorder, accelerated atherosclerosis and plaque vulnerability, platelet aggregation and thrombosis, and disruption in cardiac structure and function, ultimately leading to cardiovascular events and death. Therein, oxidative stress and inflammation were suggested to play pivotal roles in those pathophysiological processes.

CONCLUSION

Those biology mechanisms have deepen insights into the etiology, course, prevention and treatment of this public health concern, although the underlying mechanisms have not yet been entirely clarified.

Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021

Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: "Air pollutant source", "Exposure-Response relationship", "Public & Occupational Health", and so on. Future research hotspots are analyzed through characteristics of the cluster groups, including the advancement of health risk assessment techniques, an interdisciplinary approach to quantifying human exposure to air pollution, and strategies in health risk assessment.

A benefit allocation model for the joint prevention and control of air pollution in China: In view of environmental justice

The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread "free rider" behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO₂ governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO₂ control and can be extended to the researches and practices of other air pollutants control.

Year-round observation of atmospheric inorganic aerosols in urban Beijing: Size distribution, source analysis, and reduction mechanism

To investigate particle characteristics and find an effective measure to control severe particle pollution, year-round observation of size-segregated inorganic aerosols was conducted in Beijing from January to December, 2016. The sampled atmospheric particles all presented bimodal size distribution at four pollution levels (clear, slight pollution, moderate pollution and severe pollution), and peak values appeared at the size range of 0.7-2.1 μm and >9.0 μm, respectively. As dominant particle compositions, NO₃^-, SO₄^2-, and NH₄⁺ in four pollution levels all showed significant peaks in fine mode, especially at the size range of 1.1-2.1 μm. Secondary inorganic aerosols accounted for about 67.6% (36.3% (secondary sulfates) + 31.3% (secondary nitrates)) of the total sources of fine particles in urban Beijing. Severe pollution of fine particles was mainly caused by the air masses transported from nearby western and southern areas, which are industrial and densely populated region, respectively. Sensitivity tests further revealed that the control measures focusing on ammonium emission reduction was the most effective for particle pollution mitigation, and fine particles all showed nonlinear responses after reducing ammonium, nitrate, and sulfate concentrations, with the fitting curves of y = -120.8x - 306.1x² + 290.2x³, y = -43.5x - 67.8x², and y = -25.8x - 110.4x² + 7.6x³, respectively (y and x present fine particle mass variation (μg/m³) and concentration reduction ratio (CRR)/100 (dimensionless)). Overall, our study presents useful information for understanding the characteristics of atmospheric inorganic aerosols in urban Beijing, as well as offers policy makers with effective measure for mitigating particle pollution.

Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.

A hybrid deep learning technology for PM2.5 air quality forecasting

The concentration of PM_2.5 is one of the main factors in evaluating the air quality in environmental science. The severe level of PM_2.5 directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of the air quality, it is difficult to predict the volatile changes of PM_2.5 over time. In this paper, a hybrid deep learning model VMD-BiLSTM is constructed, which combines variational mode decomposition (VMD) and bidirectional long short-term memory network (BiLSTM), to predict PM_2.5 changes in cities in China. VMD decomposes the original PM_2.5 complex time series data into multiple sub-signal components according to the frequency domain. Then, BiLSTM is employed to predict each sub-signal component separately, which significantly improved forecasting accuracy. Through a comprehensive study with existing models, such as the EMD-based models and other VMD-based models, we justify the outperformance of the proposed VMD-BiLSTM model over all compared models. The results show that the prediction results are significantly improved with the proposed forecasting framework. And the prediction models integrating VMD are better than those integrating EMD. Among all the models integrating VMD, the proposed VMD-BiLSTM model is the most stable forecasting method.

Application of a health index using PM2.5 concentration reductions for evaluating cross-administrative region air quality policies

The primary goal of air quality policies is to reduce the impact of air pollution on human health, in particular, it is very important in the attainment-closing geographic areas with densely populated like Taiwan. Air quality policies in general only consider the reduction of emission as well as concentration, in order to highlight the effects of air pollution reduction responded on human health, a health index using PM_2.5 concentration reductions (HI-cr) was adopted to evaluate air quality policies in this study. To investigate priorities of cross-administrative region air quality policies, the HI-cr were calculated by using annual results of atmospheric modeling and air quality modeling, which involved the meteorological effect and spatial distribution of air pollution. By studying the emission reduction targets of three administrative regions, Taichung, Changhwa, and Nantau in the Central Taiwan, 8 reduction scenarios were designed and examined. It is found that HI-cr can present detailed information in human health.From the results of adaptability assessement based on health, the equal reduction of emission in present air quality policy gave the most concentration reduction area with lower HI-cr. But according to the analysis in different proportion of emission reduction scenarios, it found that emission reduced more in the most population region, even the concentration reduction is not the highest but HI-cr increased. According to the analysis of different emission reduction policies, this study suggests HI-cr is an important index to evaluate the air pollution control policies instead of considering the impact of air pollutant concentrations only, especially in cross-administrative regions.Implications: In this study, we present a modified health index, HI-cr, to determine the priority of cross-administrative air quality policies using PM_2.5 concentration reduction. HI-cr is adaptable for any types of geography, in particular for areas where the air quality is almost attainment like Taiwan. Cross-administrative air quality policies could be evaluated using HI-cr, it could highlight the high performance on population health improvement rather than high concentration reduction. In particular, for economies where the air quality is almost attainment and with complex terrain and dense population, air quality policies should consider the health prevention issues.

The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction

Understanding the intra-city variation of PM_2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM_2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R², adjust R² and 10-fold cross validation R² for the annual PM_2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM_2.5 and controlling vehicle emissions would be an effective way to reduce PM_2.5. The predicted PM_2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM_2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM_2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM_2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM_2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM_2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m³. Our results are expected to provide valuable information for further air pollution control strategies in China.

Health benefits of achieving fine particulate matter standards in India - A nationwide assessment

BACKGROUND

Ambient fine particulate matter (PM_2.5) is one of the leading risk factors in India. The elevated levels of PM_2.5 exposure concentration in India are related to higher premature mortality. However, health benefits or avoidable premature mortality by reducing PM_2.5 concentration is uncertain.

OBJECTIVES

Here we simulated the health benefits by assuming the achievement of 1) National Ambient Air Quality Standards of India (PM_2.5 annual average = 40 μg m^-3), 2) National Clean Air Programme policy (30% reduction) and 3) World Health Organization standard (10 μg m^-3).

METHODOLOGY

Using Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE), the health benefits are estimated at national, state and district levels for various health endpoints viz., all-cause, ischaemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer and stroke. PM_2.5 data, concentration-response coefficient, population, and baseline incidence rate are specified as input data in BenMAP-CE.

RESULTS

At the national level, all-cause health benefits in three simulations range from 0.79 to 2.1 million cases during 2019. Similarly, IHD, COPD, lung cancer, and stroke related health benefits are in the range of 0.28-0.68, 0.17-0.39, 0.01-0.03, and 0.14-0.34 million cases, respectively. State-level estimates showed that Uttar Pradesh, Bihar, and West Bengal are having maximum health benefits whereas north-eastern states are found with lowest estimates. Districts such as Allahabad, Lucknow, Muzaffarpur, Patna, and Sultanpur are estimated to have highest health benefits. States and districts with higher PM_2.5 concentration and exposed population are found with maximum health benefits. Among the three simulations, achievement of the World Health Organization standard resulted in highest estimates. Further, the limitations and sensitivity of input parameters used in this study are discussed in detail.

CONCLUSION

Study results highlighted the need for state and district-specific air quality management measures to increase PM_2.5 related health benefits.

Regional Policies Targeting Residential Solid Fuel and Agricultural Emissions Can Improve Air Quality and Public Health in the Greater Bay Area and Across China

Air pollution exposure is a leading public health problem in China. The majority of the total air pollution disease burden is from fine particulate matter (PM2.5) exposure, with smaller contributions from ozone (O3) exposure. Recent emission reductions have reduced PM2.5 exposure. However, levels of exposure and the associated risk remain high, some pollutant emissions have increased, and some sectors lack effective emission control measures. We quantified the potential impacts of relevant policy scenarios on ambient air quality and public health across China. We show that PM2.5 exposure inside the Greater Bay Area (GBA) is strongly controlled by emissions outside the GBA. We find that reductions in residential solid fuel use and agricultural fertilizer emissions result in the greatest reductions in PM2.5 exposure and the largest health benefits. A 50% transition from residential solid fuel use to liquefied petroleum gas outside the GBA reduced PM2.5 exposure by 15% in China and 3% within the GBA, and avoided 191,400 premature deaths each year across China. Reducing agricultural fertilizer emissions of ammonia by 30% outside the GBA reduced PM2.5 exposure by 4% in China and 3% in the GBA, avoiding 56,500 annual premature deaths across China. Our simulations suggest that reducing residential solid fuel or industrial emissions will reduce both PM2.5 and O3 exposure, whereas other policies may increase O3 exposure. Improving particulate air quality inside the GBA will require consideration of residential solid fuel and agricultural sectors, which currently lack targeted policies, and regional cooperation both inside and outside the GBA.

Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure and related health impacts, especially in China that experiences both PM_2.5 and O₃ pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM_2.5 and O₃. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM_2.5 and O₃ in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NO_x, SO₂, NH₃, VOC and primary PM_2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O₃ and PM_2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO₂ in January, April and October, and SO₂ in April, but overestimate NH₃ in April and VOC in January and October. Primary PM_2.5 emissions are also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH₃ observations to improve the RSM-assimilation performance and emission inventories.

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM₁₀ and PM_2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM_2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM_2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM_2.5 among 'children' age group than for PM₁₀ (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM_2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 μg m-3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

Quantifying public health benefits of PM2.5 reduction and spatial distribution analysis in China

In recent years, particulate matter (PM) air pollution has become a significant and growing public health problem in China. In this study, the daily PM_2.5 exposure level at a spatial resolution of 100 km² was simulated based on the data of 1328 monitoring sites and the Voronoi Neighborhood Averaging (VNA) interpolation method. The results reveal that the daily mean PM_2.5 concentration reduced from 47.82 μg/m³ (2016) to 40.87 μg/m³ (2018), a reduction of 14.53%. We first calculated the heath impacts and economic benefits of this reduction (Scenario 1) by using Environmental Benefits Mapping and Analysis Program (BenMAP). The estimated avoided premature mortalities for all-cause, cardiovascular diseases, respiratory diseases, and lung cancer were in the range of 7214 to 81,681 cases (total of 154,176 cases). The estimated economic benefits based on willingness to pay (WTP) ranged from 3.96 to 44.85 billion RMB (total of 84.66 billion RMB). Moreover, the PM_2.5 concentration in the control scenario was rolled back to the Grade I standards (35 μg/m³, Scenario 2). The avoided deaths are in the range of 58,820 to 590,464 cases (total of 1,217,671 cases). The estimated monetary value of the avoided cases of all health endpoints range from 36.63 to 367.66 billion RMB based on WTP (total of 758.21 billion RMB). In addition, the spatial autocorrelation analysis reveals that the distribution of both avoided premature mortality and economic benefits exhibit a certain spatial aggregation.

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

Impact of Urbanization on PM2.5-Related Health and Economic Loss in China 338 Cities

According to the requirements of the Healthy China Program, reasonable assessment of residents' health risks and economic loss caused by urban air pollution is of great significance for environmental health policy planning. Based on the data of PM2.5 concentration, population density, and urbanization level of 338 Chinese cities in the year of 2015, the epidemiological relative risk (RR) was adopted to estimate the negative health effects caused by exposure to PM2.5. Meanwhile, the Value of Statistical Life (VSL) and Cost of Illness (COI) methods were used to calculate economic loss. The results show that PM2.5 pollution remains serious in 2015, which brings about many people suffering from all kinds of fearful health problems especially premature death and related diseases. The mortality and morbidity increase dramatically, and the total direct economic loss related to PM2.5 pollution in 2015 was 1.846 trillion yuan, accounting for 2.73% of total annual GDP. In addition, there was a strong correlation between urbanization level and health risks as well as economic loss, which implies that people who live in highly urbanized cities may face more severe health and economic losses. Furthermore, 338 cities were divided into four categories based on urbanization level and economic loss, of which the key areas (type D) were the regions where an increase in monitoring and governance is most needed. In the process of urbanization, policy makers should pay more attention to health costs and regional differentiated management, as well as promote the construction of healthy cities more widely.

PM2.5-Related Health Economic Benefits Evaluation Based on Air Improvement Action Plan in Wuhan City, Middle China

On the basis of PM_2.5 data of the national air quality monitoring sites, local population data, and baseline all-cause mortality rate, PM_2.5-related health economic benefits of the Air Improvement Action Plan implemented in Wuhan in 2013–2017 were investigated using health-impact and valuation functions. Annual avoided premature deaths driven by the average concentration of PM_2.5 decrease were evaluated, and the economic benefits were computed by using the value of statistical life (VSL) method. Results showed that the number of avoided premature deaths in Wuhan are 21,384 (95% confidence interval (CI): 15,004 to 27,255) during 2013–2017, due to the implementation of the Air Improvement Action Plan. According to the VSL method, the obtained economic benefits of Huangpi, Wuchang, Hongshan, Xinzhou, Jiang’an, Hanyang, Jiangxia, Qiaokou, Jianghan, Qingshan, Caidian, Dongxihu, and Hannan District were 8.55, 8.19, 8.04, 7.39, 5.78, 4.84, 4.37, 4.04, 3.90, 3.30, 2.87, 2.42, and 0.66 billion RMB (1 RMB = 0.1417 USD On 14 October 2019), respectively. These economic benefits added up to 64.35 billion RMB (95% CI: 45.15 to 82.02 billion RMB), accounting for 4.80% (95% CI: 3.37% to 6.12%) of the total GDP of Wuhan in 2017. Therefore, in the process of formulating a regional air quality improvement scheme, apart from establishing hierarchical emission-reduction standards and policies, policy makers should give integrated consideration to the relationship between regional economic development, environmental protection and residents’ health benefits. Furthermore, for improving air quality, air quality compensation mechanisms can be established on the basis of the status quo and trends of air quality, population distribution, and economic development factors.

Production of an effective catalyst with increased oxygen vacancies from manganese slag for selective catalytic reduction of nitric oxide

Manganese slag is a solid waste produced by the steel industry and usually lacks a proper recycling. In this paper, the manganese slag was used to synthesize a catalyst via microwave assistant method and applied in selective catalytic reduction of nitric oxide. As a result, the catalyst exhibited an excellent low-temperature activity. It removed 78.31% of nitric oxide at 100 °C, 44.44% higher than that of a slag-derived catalyst synthesized by ammonia impregnation, and 63.18% higher than that of a commercial catalyst. Even after a hydrothermal treatment, the catalyst still showed a removal of 69.26% at 150 °C. After a detailed characterization, the low-temperature activity was attributed to an increased amount of oxygen vacancies, which were generated during the microwave synthesis. The generated oxygen vacancies provided adsorption sites for chemisorbed oxygens, which then promoted the electron transfer for reduction of nitric oxide. The main result of this work will help to develop a low-cost catalyst and obtain a high-value-added utilization of manganese slag at the same time.

A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards

PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

To estimate PM_2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM_2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R² value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m³, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM_2.5 measurements increase CV R² by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM_2.5 measurements and AOD values are among the most-important predictor variables for the training process.