Air Pollution Control Research Articles

A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model’s properties and modelling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model’s effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

Is More Always Better? Government Attention and Environmental Governance Efficiency: Empirical Evidence from China

In recent years, the thorough implementation of China’s green development concept has compelled local governments to devote more attention to environmental issues. This study aimed to verify whether increased government environmental attention (GEA) can sustainably ensure the implementation of environmental governance, particularly air pollution control. Using government work reports (GWRs) from local governments, this study employed machine learning methods to identify and quantify the attitudes of government officials as expressed in policy texts. A weighted dictionary method was used to quantify GEA from 2011 to 2016. The results of spatial econometric models indicated that air pollution exhibited positive spatial clustering effects across different regions, with the Yangtze River Delta and the Beijing–Tianjin–Hebei region being classified as high–high areas, while the western regions were classified as low–low areas. Baseline regression results showed that increased GEA can improve the effectiveness of pollution control, but excessive attention leads to a decline in governance efficiency. Overall, this study helps explain the unsustainability of campaign-style environmental governance and provides guidance for local governments on the rational allocation of attention when addressing environmental issues.

Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO2) and rising temperatures, the near-ground O3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies.

Health benefits of air pollution control policies targeting mobile vehicles and electricity generating units

Health benefits of air pollution control policies targeting mobile vehicles and electricity generating units

Variations in Cloud Concentration Nuclei Related to Continental Air Pollution Control and Maritime Fuel Regulation over the Northwest Pacific Ocean

Here, we compared the concentrations of cloud condensation nuclei (CCN) and particle number size distributions (PNSDs) measured during the transient period from the winter to the summer East Asian monsoon in 2021 with those in 2014 to explore possible responses to how CCN responds to upwind continental air pollutant mitigation and marine traffic fuel sulfur content (FSC) regulation over the northwest Pacific Ocean (NWPO). We also employed the Positive Matrix Factorization (PMF) analysis to apportion concentrations of CCN (Nccn) to different sources in order to quantify its source-specified responses to mitigation of air pollution during the transient period. Our results showed that (1) upwind continental mitigation likely reduced Nccn by approximately 200 cm−3 and 400 cm−3 at 0.2% and 0.4% supersaturation (SS), respectively, in the marine background atmosphere over the NWPO; (2) FSC regulation resulted in a decrease in Nccn at 0.4% SS by about 50 cm−3 and was nearly negligible at 0.2% SS over the NWPO. Additionally, a PMF-resolved factor, characterized by a dominant nucleation mode, was present only in 2014 and disappeared in 2021, likely due to the reduction. This estimation, however, suffered from uncertainties since seasonal changes were hard to be deducted accurately. PMF-resolved factors accurately represented Nccn in 80–90% of cases, but this accuracy was not observed in the remaining cases. Finally, an integrated analysis of satellite-derived cloud parameters and ship-based measurements indicated that the reduced Nccn over the NWPO might be co-limited with meteorological factors in forming cloud droplets during the transient period.

Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China.

China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.

Coastal Forum: The latest sea level rise projections of the Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change (IPCC) is a multi-country organization of the United Nations formed in 1988 to assess science related to climate change (IPCC 2024). In March 2023, the IPCC published its Sixth Assessment Report (AR6), its sixth assessment of climate change since its first in 1990 (IPCC 2023). IPCC (2023) and its supporting reports total almost 8,000 pages (Boehm and Schumer 2023). Hundreds of scientists from around the world worked to develop the IPCC reports, and thousands of scientists peer-reviewed the reports(Boehm and Schumer 2023; Houston 2022).A chapter of AR6 deals with the physical basis for climate change and includes sea level rise projections (Fox-Kemper et al. 2021). These projections were supported by updated projections of the contributions to sea level rise from Antarctica (Oppenheimer 2019) and Greenland (The IMBIE Team 2020). IPCC (2023) presents the final projections. Updated IPCC projections are not expected until its seventh assessment in 2029-2030. I will report on the sea level projections in AR6, and on an online tool that provides sea level rise projections for the world as a whole and at individual tide gauge locations around the world. The IPCC bases its projections on climate response to five scenarios that cover the range of possible future anthropogenic drivers of climate change. “These scenarios combine socio-economic assumptions, levels of climate mitigation, land use, and air pollution controls for aerosols” (Climate Neutral Group 2021). IPCC then determines levels of greenhouse gases(largely carbon dioxide) and temperatures to 2050 and 2100 using complex calculations that depend on how quickly humans curb greenhouse gas emissions. It estimates sea level rise using projected temperatures. Scenarios based on IPCC (2023) are summarized below.

Environmental regulation and worker earnings: Evidence from city-level air quality standards in China

This study delves into the influence of environmental regulations on worker earnings, employing China's City Air Pollution Prevention and Control Program (CAPPCP) as a quasi-experimental framework. We employ a difference-in-differences (DD) approach and match two unique firm-level data sets that include information on firm's economic indicators, and various emissions and abatement equipment. The results show that CAPPCP causes an average of 3.2% reduction in worker earnings, which amounts to CNY 6.1 billion (USD 803.4 million) in forgone earnings. The estimated result is less than that of comparable US studies. In addition, the reduction in earning tends to be attributed at bonuses rather than regular wages, and firms pay more unemployment insurance for employees. In the channel test, CAPPCP leads to an increase of 4.7% in firm's pollution abatement equipment and 6.4% in pollution treatment capacity, which significantly increase firm's production costs. We also find different types of firm responses to the regulations, where high-polluting, smaller, labor-intensive and non-SOE firms show a higher reduction in worker earnings than their counterparts. A cost-benefit analysis suggests that the health benefits gained through CAPPCP significantly outweigh that of earning losses.

Environmental forcing and policy synergy: A multidimensional approach in the governance of air pollution and carbon emission

Policy synergies effectively contribute to the integrated management of air pollution and carbon emissions, which is crucial for safeguarding ecosystem stability and public health. This study uses the causal network model of Gaussian process regression to analyze the combined impacts of dynamic and static carbon emission reduction and air quality policies on carbon emissions and air quality. The causal effects of policy measures and their synergistic effects are also examined. The study results indicate: (1) There is significant geographical heterogeneity in the implementation of environmental policies and regional economic development, with the economically developed eastern coastal regions adopting more stringent carbon emission and air pollution control measures, while the western provinces adopt relatively lax environmental policies. (2) The synergistic effect of carbon emission reduction policies and air quality policies exists, and the two types of static policies are substitutable for managing carbon dioxide emissions and air pollution. (3) Policies' forced effect exists, where the exacerbation of environmental problems leads to the formation and implementation of policies. (4) The value added by the secondary industry is a key motivation for forming carbon emission reduction policies and air quality control policies. Additionally, the value added by the secondary industry directly impacts the incidence of respiratory diseases (e.g., tuberculosis). Finally, dynamic and synergistic policy recommendations are proposed based on the study's findings.

Evaluation of the air quality and the thermal comfort in the kitchen fume environment under the fresh air supplement

Kitchen cooking particles can severely deteriorate indoor environment, aggravating the risk of cardiovascular and cerebrovascular diseases, as well as respiratory diseases. In view of the severe hazard of cooking particles to human health, this paper numerically studies the trajectory and deposition of the cooking particles in the kitchen environment based on a numerical model. Also, quantitative evaluations for the air quality and thermal comfort are performed, hoping to provide a useful reference for the control of kitchen air pollution. Firstly, a kitchen environment simulation model is established based on Fluent. A post-processing UDF program for evaluating the effectiveness of fume absorption is developed. The reliability of the numerical model is verified by comparing the results of the fume concentration with those by the cooking experiments. Using this numerical model, the fume extraction effectiveness of the range hood under different velocities of the fresh air is analyzed comprehensively, including particle trajectory, air age, temperature, PMV (Predicted Mean Vote), and PPD (Predicted Percentage of Dissatisfied) indicators. Results have shown that high speed of the fresh air can cause a strong blowing sensation, deteriorating indoor air quality. The PMV and PPD values around the human body show a parabolic trend. Generally, the relatively low fresh air speed can achieve satisfactory air quality and thermal comfort while saving energy consumption.

Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in five levels. Time Frame Return Frequency TextRank text classification method based on key content extraction and text classification model based on Convolutional Neural Network and Bidirectional Encoder Representations from Transforms models were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

Mitigation of PBDE net discharge in hazardous waste thermal treatment system through reintroducion of sludge and fly ash into GASMILD operations

The presence of polybrominated diphenyl ethers (PBDEs) in consumer products, waste treatment processes, and treated ashes poses a significant environmental threat. Due to the lack of research on the removal of PBDEs during waste incineration, this study investigated the effectiveness of a Hazardous Waste Thermal Treatment System (HAWTTS) utilizing reburning of sludge and fly ash (SFA) with gasification-moderate or intense low-oxygen dilution (GASMILD) combustion for PBDE removal. The closed-loop treatment of sludge and ash within the HAWTTS provides a potential pathway for near-zero PBDE emissions. The GASMILD combustion addresses potential combustion issues associated with fly ash recirculation. The system achieved an impressive overall removal efficiency of 98.4% for PBDEs, with minimal stack emissions (2.45 ng/Nm³) and a negative net discharge rate (−1.02 μg/h). GASMILD combustion played a crucial role (92.7%–97.6% destruction) in addressing challenges associated with high-moisture feedstocks and SFA residues. Debromination of highly brominated PBDEs occurred within the incinerator, resulting in an increased proportion of lower brominated PBDEs in the bottom slag compared to the feedstock. Air Pollution Control Devices (APCDs) achieved a total PBDE removal efficiency of 74.4%. However, the hydrophobic nature of PBDEs limited removal efficiency in scrubbers (36.0%) and cyclonic demisters (37.86%). This study demonstrates that reintroducing SFA into the GASMILD combustion process offers an effective and environmentally sustainable strategy for reducing net PBDE levels in hazardous waste. This approach also provides additional benefits such as energy conservation, reduced carbon emissions, and lower operating costs associated with secondary treatment of thermally treated byproducts.

Long-term trends and probability distribution functions of air pollutant concentrations in the megacity of São Paulo

Air quality conditions in many urban areas have improved in the last decades as a consequence of air pollution control policies and regulations. Mitigation strategies have been successful in reducing the concentration of primary pollutants like inhalable particulate matter (PM10), but the control of secondary pollutants like tropospheric ozone (O3) is still challenging in megacities like the Metropolitan Area of São Paulo (MASP) in Brazil. To support the development of effective mitigation strategies, it is crucial to characterize the statistical behavior of air pollutant concentrations and its long-term evolution. Probability Density Functions (PDF) can be useful to model site-specific air quality conditions, providing estimates for the frequency of extreme pollution events and exceedance of air quality standards. The current study aims to characterize which PDF model better fits and characterizes the variability of PM10 and O3 concentrations in the MASP. For that, daily maximum moving average concentrations were analyzed between 2000 and 2023, characterizing the long-term trends and the frequency of exceedance of air quality standards. PM10 concentrations followed a lognormal PDF, with an expected value of 31 ± 15 µg.m-3. O3 followed a Gamma PDF, with an expected value of 68 ± 25 µg.m-3. A consistent long-term decrease was observed for PM10 (-1.04 ± 0.09 µg.m-3.yr-1), while O3 showed an increasing trend of 0.51 ± 0.04 µg.m-3.yr-1 in the summer. In recent years (2021-2023), the probability of exceedance of the World Health Organization guideline was 17.4 and 11.0%, respectively, for PM10 and O3. In 2020, a statistically significant increase in O3 expected values was observed, possibly associated with changes in the emission patterns of precursors due to the mobility restrictions imposed by the COVID-19 pandemic.

Can Energy-Consuming Rights Trading Policies Help to Curb Air Pollution? Evidence from China

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China’s energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 µm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 µm, which are most harmful because they can reach the alveoli, whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 µm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition of particles captured by leaves. Moreover, we identified how specific leaf wax compositions—beyond the known sticky nature of hydrophobic waxes—contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles.

Research on Evolution and Recommendations of China’s PM2.5 and O3 Pollution Control Policies under the Carbon Peak and Carbon Neutrality Targets

In recent years, the issue of PM2.5 and O3 pollution in China has gradually become a hot topic in air pollution control. Powerful, precise, and clear policies are important to guide the efficient control of PM2.5 and O3 pollution. However, research on PM2.5 and O3 pollution policies is very limited. Moreover, these reports have failed to identify all the specific characteristics of the PM2.5 and O3 pollution policies in China, leading to inefficient control of PM2.5 and O3 pollution. Therefore, based on the external features and internal structures of relevant policy documents, this paper conducts a detailed bibliometric analysis to elucidate the evolution of PM2.5 and O3 pollution control policies in China. The results reveal the following: (1) The PM2.5 and O3 pollution control policies in China exhibit a characteristic of “top-down cross-level response lag”, transitioning from individual pollutant control to coordinated control of PM2.5 and O3 pollution. (2) The Ministry of Environmental Protection and the Ministry of Ecology and Environment have been the two major policy issuers in China’s government, focusing on “Technology” and “Monitoring”, respectively. However, the aforementioned policies are mainly implemented by local governments, with limited interregional cooperation but an overemphasis on enterprise pollution control and emergency systems. (3) In terms of policy instruments, supply- and environment-oriented policy instruments are predominant, whereas demand-oriented policy instruments are inadequate. Therefore, it is suggested to utilize the synergistic effect of pollution reduction and carbon mitigation to achieve the goal of the “dual carbon” strategy, enhance public participation to strengthen cooperation among diverse stakeholders, strengthen cross-regional cooperation to overcome governance barriers, and reasonably optimize the use of policy instruments to form an effective combination of policy instruments.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

The screening evaluation of environmental odors: a new dispersion modelling-based tool.

Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed.

Assessment of the status of PM2.5 and ground-level O3 pollution variations and meteorological factor effects: A case study in Long An Province, Vietnam

Abstract Long An Province has a favorable geographical position in socio-economic development exchanges and is a fundamental bridge between the area of Ho Chi Minh City (HCMC) and the Southeast localities with the Southwestern provinces. Along with the master plan for socio-economic development with a vision for 2030, the province’s air quality issues, especially PM2.5 and surface O3 pollution, will be complex and pose many challenges to effective air pollution management and control. This study aims to investigate the effects of major meteorological characteristics (temperature–T, relative humidity–RH, surface pressure–P, and wind speed–WS) on temporal changes in the correlation between PM2.5 and surface O3 concentrations in Long An province in the 2018-2020 period (computed by a system of coupled WRF/CMAQ models) through trend evaluation using by Mann-Kendall (MK) Test and Theil-Sen Slope, and Pearson correlation analysis. The study results reported that in terms of temporal variations, both value levels of PM2.5 and O3 in the dry season tended to surpass that in the rainy season. Moreover, PM2.5 levels tended to exceed the 24-hour mean threshold (between 1.001 and 3.14 times) and the annual mean threshold (from 1.81 to 2.03 times) of the Vietnamese National Ambient Air Quality Standard. Based on the Pearson correlation analysis, significant influences of meteorological conditions on the pollution status of PM2.5 and surface O3 were also demonstrated, such as the different positive and negative correlations of each meteorological factor on concentration. These outstanding results serve as an essential reference for developing air quality targets and proposing future air pollutant control measures in Long An Province.