Air Pollution Control Strategies Research Articles

How Have Emissions and Weather Patterns Contributed to Air Pollution in Lanzhou, China?

Air quality is predominantly influenced by two factors: emission sources and meteorological conditions. Understanding their relative contribution is essential for developing effective air pollution control strategies. Two rounds of lockdown measures in Lanzhou during the winter of 2021 and 2022 offered a valuable opportunity to reveal the impact of pollutant emissions and meteorological conditions on air pollution events. The reduction in emissions during the pandemic lockdown period (2021–2022) resulted in a 36.05% decrease in PM2.5 concentrations compared to the historical period of 2014–2020. Using ERA5 reanalysis meteorological data and principal component analysis, weather patterns were classified into three distinct types: favorable for pollutant accumulation (FPA), unfavorable for pollutant accumulation (NFP), and neutral condition (NTL). A comparative analysis of pollutant concentrations, frequency, and duration of each weather type during the lockdown and historical periods revealed that weather types had a minimal impact on pollutant levels, with emissions serving as the dominant factor. Nevertheless, the occurrence of FPA was often linked to severe pollution events, suggesting a positive feedback loop between severe pollution and FPA weather type. This indicated that FPA can lead to severe pollution events and more severe pollution may be associated with prolonged FPA durations. These findings suggest that identifying FPA weather patterns can significantly inform the implementation of air pollution control measures to mitigate air pollution levels.

Contribution and Mechanisms of BVOCs Emitted From a Typical Large Lake With Algal Bloom to O3 Levels in Lakeside Urban Areas

AbstractLakes near developed cities often experience algal blooms due to eutrophication, leading to odor and biogenic emissions that can potentially interfere with the formation of air pollutants such as O3 around these lakes. However, the impact and its pathways remain poorly understood. This study investigates the contributions of biogenic volatile organic compound (BVOC) emissions from Lake Taihu to O3 formation in surrounding regions using a numerical approach. Composite analysis, case studies, and process analysis reveal that BVOC emissions from Lake Taihu increase hourly near‐surface O3 concentrations, with a maximum rise of 25.5 μg m−3, improving model performance. The influence of lake BVOC emissions is large on the northwest side of Lake Taihu, primarily determined by the spatial emission patterns and dominant wind fields. The development of the planetary boundary layer, particularly the thermal internal boundary layer, and the enhanced lake breeze play important roles in increasing near‐surface O3 levels over land, whereas variations in temperature play a minor role. Vertical cross‐sections show that interactions between land‐lake breezes and background wind fields are critical for O3 transport, increasing surface concentrations in surrounding areas. Additionally, lake BVOC emissions intensify both daytime and nighttime gas‐phase chemical formation of O3, consuming nitrates and producing additional O3 within the lower troposphere. This study highlights the importance of local thermal circulations and lake BVOC emissions in O3 pollution, emphasizing the need for comprehensive air pollution control strategies in lakeside cities.

A demographic optimization proximity model for air pollution exposure assessment

Air pollution poses a significant threat to human health. Effective and efficient assessing individual exposure intensity will provide a crucial reference to mitigate potential air pollution risk. Previous proximity models in scenario simulation method neglect the variations among individual absorption efficiency. In this study, we proposed a novel demographic optimization proximity model (DOPM) to quantified sulfur dioxide (SO2) exposure risk under eight groups’ respiratory rates. In assessing exposure risk in Wuhan, one of the megacities in central China, we compared the capacity of DOPM and a classic proximity model on simulating exposure intensity, utilizing the near-Gaussian bi-square diffusion function and the filter optimal bandwidth. Subsequently, we utilized ordinary kriging (OK) interpolation to visualize the SO2 exposure risk map based on the simulation results of DOPM. We found that 9.5 km was the optimal bandwidth when near-Gaussian bi-square diffusion function utilized in proximity model. We also observed DOPM has stronger ability to simulate individual exposure intensity with a R-square of 0.44 when distinguished respiratory rate among receptors. These insights will aid public health researchers in assessing exposure risk across absorption efficiency and help local government officials devise more effective air pollution control strategies.

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.

Atmospheric Estrogenic Semi-Volatile Compounds and PAH in PM2.5 in Mexico City

The quantification of semi-volatile organic compounds with potential endocrine-disrupting activity contained in fine atmospheric particles (PM2.5) is essential to understand their temporal behavior, identify their sources, and evaluate the health risks resulting from population exposure to said compounds. Since information and research outcomes regarding their presence in the atmosphere in developing countries are scarce, the main objective of this work was the development of a methodology devoted to extracting, characterizing, and quantifying, for the first time in Mexico, the concentration levels of three important groups of endocrine-disrupting compounds (EDCs) bonded to PM2.5 and collected during a year, namely: alkylphenols (4-n-nonylphenol (4NP) and 4-tert-octylphenol (4tOP)); bisphenols (bisphenol A (BPA) and bisphenol F (BPF)); natural and synthetic hormones (17β-estradiol (E2), estriol (E3) and 17α-ethinyl estradiol (EE2)). Further, priority polycyclic aromatic hydrocarbons (PAH) that also disrupt endocrine activity were analyzed. All compounds were determined by gas chromatography coupled to tandem mass spectrometry, and the concentration levels were analyzed for different climatic seasons. Cold-dry (CD) season displayed higher levels of 4NP, bisphenols, and hormones (between 0.71 (4NP) and 1860 pg m−3 (BPA)), as well as PAH concentrations (9.12 ng m−3). Regarding health effects, concentrations of alkylphenols, bisphenols, and hormones quantified had a value of estradiol equivalent concentration (EEQE2) between 0.07 and 0.17 ng m−3. PAH concentrations did not have carcinogenic and mutagenic risk with BaP(PEQ) < 1 ng m−3. These results can be used by policymakers in the design of strategies for air pollution control.

Southern California ozone exposure disparities under different emissions control strategies in a low-carbon future.

Southern California ozone exposure disparities under different emissions control strategies in a low-carbon future.

Motorcycle Emission Simulation Using AVL Cruise M Software

The study utilizes AVL Cruise M software to simulate emissions and fuel consumption from the Honda Future 125 Fi motorcycle, a popular model in Vietnam. Simulations were conducted under three standard driving cycles: World Motorcycle Test Cycle (WMTC), European Motorcycle Driving Cycle (ECE R40), and Hanoi Motorcycle Driving Cycle (HMDC) aiming to assess CO, HC and NOx emissions under different operational conditions. The results demonstrate that each driving cycle has a distinct impact on emission levels, with significant differences in CO, HC, and NOx concentrations across the cycles. The HMDC cycle, which reflects typical traffic conditions in Vietnam, is identified as a suitable choice for simulating motorcycle emissions in urban areas. These findings provide an additional tool for calculating motorcycle emissions, supporting the development of more effective air pollution control strategies in the future.

A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment.

A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment.

Influence of Air Quality Model Parameters on Pollution Concentration

Air pollution poses a significant threat to public health, ecosystems, and the global climate. Accurate prediction and effective management of air quality are of paramount importance, which, in turn, rely on sophisticated air quality models. These models integrate a variety of atmospheric parameters to simulate the dispersion of pollutants in the complex urban atmosphere, yet the influence of specific input parameters on predicted pollution concentrations has not been fully elucidated. This comprehensive study assesses how variations in model input parameters can lead to divergent pollution concentration outputs, with the goal of identifying those that are most critical to model accuracy. Using observational data from air quality monitoring stations in conjunction with meteorological records, the study explores the sensitivity of forecasted pollutant concentrations to fluctuations in model inputs such as emission source strength, atmospheric stability, wind speed and direction, diurnal heating patterns, chemical reaction rates, and boundary layer dynamics. Dispersion models are evaluated across different spatial and temporal scales to gauge their response to environmental variables and topographic features. The performance of these models is also assessed against satellite-derived pollutant measurements to encompass a broader geographical context. Through the application of numerical simulations and statistical analyses, the study quantifies the relative impact of each parameter. Cross-validation techniques, along with uncertainty quantification methods, are applied to ensure the reliability of the conclusions drawn. The research also incorporates the use of machine learning tools to identify complex patterns in the environmental data that may be missed by traditional modeling approaches. The abstract concludes that a detailed understanding of influential model parameters is essential for refining air quality predictions. Improvements in the accuracy of dispersion models will enable policymakers and urban planners to make better-informed decisions regarding air pollution control and mitigation strategies. This work forms the foundation for future advancements in the field of atmospheric sciences and encourages continued exploration into the interaction between anthropogenic activities, meteorological phenomena, and air quality outcomes

Chemical characterization and source identification of PM2.5 in the Huaxi urban area of Guiyang

In 2020, 123 PM2.5 samples were collected across different seasons in Huaxi District, Guiyang. The primary chemical components of PM2.5, including water-soluble ions (WSIIs), metallic elements, organic carbon (OC), and elemental carbon (EC), were analyzed. During the sampling period, the average PM2.5 concentration was 39.7 ± 22.3 µg/m2. Chemical mass closure (CMC) was used to reconstruct PM2.5 mass, yielding a reconstructed concentration of 29.1 ± 16.5 µg/m2. The major components were organic matter (OM), sulfate + nitrate + ammonium (SNA), and mineral dust (MD), with mean concentrations of 12.2 ± 6.3 µg/m2, 8.2 ± 4.0 µg/m2, and 6.3 ± 4.6 µg/m2, respectively. From clean days (CD) to lightly-moderately polluted days (LMPD), nitrate oxidation ratio (NOR) increased from 0.09 to 0.16, while sulfate oxidation ratio (SOR) and OC/EC ratio rose by 21.7% and 13.5%, indicating stronger secondary reactions on polluted days. The study also examined changes in chemical components under different atmospheric oxidizing and humidity conditions, revealing that sulfate and nitrate concentrations increased with relative humidity (RH) between 60 and 80%, while other components, especially MD, showed a declining trend due to hygroscopic growth and subsequent gravitational settling and precipitation. The average NO3−/SO42− ratio was 0.67, indicating that fixed sources such as industrial and coal emissions were the main contributors to PM2.5. This study provides insights into the chemical composition, pollution processes, and formation mechanisms of PM2.5, which are crucial for developing effective air pollution control strategies. Furthermore, source apportionment was conducted with the positive matrix factorization (PMF) model. The Coal combustion, secondary, traffic, Industrial and dust source contributions to the PM2.5 mass were approximately 30.5%, 20.0%, 18.3%,16.7% and 14.5%, respectively.

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

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

Life Course Associations Between Ambient Fine Particulate Matter and the Prevalence of Prediabetes and Diabetes: A Longitudinal Cohort Study in Taiwan and Hong Kong.

Both air pollution and diabetes are key urban challenges. The association between particulate matter with a diameter of <2.5 μm (PM2.5) exposure and prediabetes/diabetes in adults is well documented, but the health effects of life course exposure remain unclear. This study evaluated the impact of PM2.5 exposure throughout various life stages on the prevalence of prediabetes/diabetes in adulthood. We included 4,551 individuals with 19,593 medical visits from two open cohorts in Taiwan and Hong Kong between 2000 and 2018. Ambient PM2.5 exposure was assessed using a satellite-based model, delivering a 2-year average exposure at a resolution of 1 km2. Logistic mixed-effects models were used to investigate longitudinal associations between PM2.5 exposure and the prevalence of prediabetes/diabetes. Life course models were used to examine the impact of PM2.5 exposure at different life stages on prediabetes/diabetes in adulthood. Over an average follow-up period of 9.93 years, 1,660 individuals with prediabetes/diabetes were observed. For the longitudinal association, every 10 μg/m3 increase in PM2.5 was associated with an increased odds of having prediabetes/diabetes (odds ratio 1.32, 95% CI 1.13, 1.54). The odds of adulthood prediabetes/diabetes increased by 15%, 18%, and 29% for each 10 μg/m3 increase in PM2.5 exposure during school age, adolescence, and adulthood, respectively. Our findings suggest a link between PM2.5 exposure during each life stage and the prevalence of prediabetes/diabetes in adulthood, with the health impacts of exposure during adulthood being slightly greater. This study underscores the need for life course air pollution control strategies to mitigate the substantial disease burden of diabetes.

Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China

Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies.

Quantitative Decoupling Analysis for Assessing the Meteorological, Emission, and Chemical Influences on Fine Particle Pollution

AbstractA comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity of the atmospheric system greatly hinders this understanding. In this study, we developed the quantitative decoupling analysis (QDA) method by applying the Factor Separation (FS) method into the model processes to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions and impact on fine particulate matter (PM2.5) pollution. Taking a heavy‐haze episode in Beijing as an example, we show that different from the integrated process rate (IPR) and the scenario analysis approach (SAA) in previous studies, the QDA method explicitly demonstrate the nonlinear effects by decomposing the variation of PM2.5 concentration into individual contributions of E, M and C terms as well as the contributions from interactions among these processes. Results showed that M dominated the hourly fluctuation of the PM2.5 concentration. The C terms increase with increasing the level of haze, reaching maximum (0.37 μg m−3 h−1) at the maintenance stage. Moreover, our method reveals that there are non‐negligible non‐linear effects of meteorological, emission, and chemical processes during pollution stage, with the mean accounting for 50% of the increase in PM2.5 concentrations, which is often ignored in the current air pollution control strategies. This study highlights that the QDA approach can be used to gain insight into the formation of heavy pollution, and to identify uncertainty in numerical models.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

FeTiO3: A low-cost and efficient photocatalytic mineral for sustainable NOx abatement

FeTiO3: A low-cost and efficient photocatalytic mineral for sustainable NOx abatement

Estimates of Air Pollutant Emissions by Vehicle Fleet in the State of Pará, Brazil: Impacts and Trends

Objective: aims to estimate emissions of the main atmospheric pollutants from vehicular sources in the state of Pará, to continue the environmental agenda of monitoring air quality. Theoretical Framework: The growing global concern about air quality in urban areas is driven by the environmental and public health impacts caused by air pollution. The concentration of pollutants from vehicle sources represents one of the main challenges, especially in densely populated regions such as the capital and other major cities in the state of Pará, where the automotive fleet has increased significantly. Method: The methodology used combined "bottom-up" and "top-down" methods to calculate emissions of carbon monoxide (CO), nitrogen oxides (NO) and total hydrocarbons (HC) for different vehicle categories and fuel types. Data on licensed vehicle fleets and fuel consumption obtained from DETRAN-PA and ANP were used. Results and Discussion: The results revealed that gasoline-powered vehicles are responsible for the majority of CO and HC emissions, while diesel vehicles emit the largest amounts of NO into the atmosphere. Motorcycles were identified as the largest source of emissions due to their larger number in the circulating fleet. Our results also highlight the importance of assessing and monitoring vehicle emissions to develop effective air pollution control strategies. Research Implications: The information obtained in this study is unprecedented for the study area, and can therefore support the formulation of environmental policies aimed at reducing emissions of atmospheric pollutants in the state of Pará, thus promoting an improvement in air quality and public health. Originality/Value: This paper makes an unprecedented contribution to national and international literature on environmental pollution in large urban centers. On the eve of the largest climate and environment conference on the planet (COP30) to be held in Pará, Amazonas, this research joins efforts in relation to the monitoring, forecasting and mitigation of air pollution, considered one of the main environmental problems of today.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown