Effective Air Quality Management Research Articles

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment

Low-cost sensors (LCS) have the potential to provide accurate and reliable measurements of air quality in real-time. This improves our ability to monitor, identify sources of pollution and develop mitigation strategies for effective air quality management. However, recent research on LCS has primarily focused on monitoring, exposure assessment, and calibration. In this study, we investigate the applicability of LCS data collected at ambient sites for characterizing and apportioning aerosol sources. Non-negative matrix factorization (NMF) was applied to the size-resolved data collected across five sites within the Indian Institute of Technology Bombay (IITB) campus in Mumbai using the LCS Alphasense OPC-N2. The sampling was done for 15 days at 5 locations in IITB, and each site only had 3 days of data. NMF resolved two factors for three sites, namely aromas (S2), hostel hub (S3) and central library (S4), while three factors were resolved for two sites, namely construction site (S1) and main gate (S5). Two common sources were determined for all the sites: (i) dust and marine source and (ii) traffic and combustion sources, which agree with the sources identified by studies in the literature. The third factor resolved at sites S1 and S5 is representative of heavy-duty diesel vehicles (HDDVs), which is present for a very short period and is captured because of the capability of high temporal resolution of the LCS. This offers a unique, cost-effective advantage of LCS for capturing episodic activities. The study suggests that in low- and middle-income countries with limited air quality monitoring capabilities, the size-time-resolved PM concentration data obtained from a network of low-cost sensors can estimate the pollution sources. This study provided evidence that despite their inherent limitations, LCS can be useful in attaining interpretable information about pollution sources and recommends extensive use of LCS for source characterization in the future.

Air Quality Index Prediction Using Machine Learning

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies. Keywords: Machine Learning, Air Pollution, AQI.

Quantifying the Light-Absorption Properties and Molecular Composition of Brown Carbon Aerosol from Sub-Saharan African Biomass Combustion.

Sub-Saharan Africa is a hotspot for biomass burning (BB)-derived carbonaceous aerosols, including light-absorbing organic (brown) carbon (BrC). However, the chemically complex nature of BrC in BB aerosols from this region is not fully understood. We generated smoke in a chamber through smoldering combustion of common sub-Saharan African biomass fuels (hardwoods, cow dung, savanna grass, and leaves). We quantified aethalometer-based, real-time light-absorption properties of BrC-containing organic-rich BB aerosols, accounting for variations in wavelength, fuel type, relative humidity, and photochemical aging conditions. In filter samples collected from the chamber and Botswana in the winter, we identified 182 BrC species, classified into lignin pyrolysis products, nitroaromatics, coumarins, stilbenes, and flavonoids. Using an extensive set of standards, we determined species-specific mass and emission factors. Our analysis revealed a linear relationship between the combined BrC species contribution to chamber-measured BB aerosol mass (0.4-14%) and the mass-absorption cross-section at 370 nm (0.2-2.2 m2 g-1). Hierarchical clustering resolved key molecular-level components from the BrC matrix, with photochemically aged emissions from leaf and cow-dung burning showing BrC fingerprints similar to those found in Botswana aerosols. These quantitative findings could potentially help refine climate model predictions, aid in source apportionment, and inform effective air quality management policies for human health and the global climate.

The Examination of different Air Constituents to Ascertain Pune City's Air Quality

The city of Pune experiences poor air quality, leading to research of various air constituents in order to assess the overall quality of the air. Significant pollutants such as PM2.5, PM10 (RSPM), NO2, O3, SO2, and CO are addressed in this analysis, along with their causes and consequences. Exceeding WHO standards, levels of PM2.5 constitute a severe problem; NO2 and O3 levels constitute grounds for concern. In order to mitigate such pollutants and obtain cleaner air in Pune City, the research stresses the necessity for more difficult emission control measures, the promotion of public transportation and electric vehicles, and the supervision of industrial and construction activity. The objective of this research is to evaluate Pune City's air quality and offer insights by thoroughly examining various air quality elements. An extensive tracking strategy is analyzed for three years (2022, 2023, and 2024), considering air quality components like RSPM (PM10), PM-2.5,NO2, O3, etc. Various graphs are analyzed for the above-mentioned years and observe the growth in air quality index for Pune city. In the last three years, data has been collected from government websites on a monthly basis, and various graphs have been plotted by considering their average value. All the graphs show an increase in the air quality index. The study's findings provide valuable new information on the present state of the air quality in Pune City and emphasize the urgent need for effective air quality management measures. To mitigate air pollution, maintain public health, and maintain environmental sustainability, initiatives must concentrate on emissions control, planning for cities, enhancing public transportation, and creating green infrastructure in Pune City.

The role of civil society and good governance in effective air quality management in the South Durban Industrial Basin, KwaZulu-Natal, South Africa

The role of civil society and good governance in effective air quality management in the South Durban Industrial Basin, KwaZulu-Natal, South Africa

Estimating PM2.5 utilizing multiple linear regression and ANN techniques

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg–Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait.

In this study, we investigated the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 μm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 μg/m3) substantially exceeded the World Health Organization (WHO) guideline of 10 μg/m3. Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 μg/m3) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region.

Analysis of the particulate matter long term emissions in Romania by sectors of activities

Particulate matter (PM) in the atmosphere is highly relevant due to its far-reaching implications. These tiny solid or liquid particles affect human health, causing respiratory and cardiovascular issues, and even premature death. PM also plays a role in climate change, as some particles contribute to warming, while others have cooling effects, impacting Earth's energy balance. Additionally, PM influences visibility, transportation safety, and ecosystems by depositing on land and water surfaces, altering soil and water quality. Recognizing and monitoring the emissions and sources of PMs by 4 subclasses, from PM2.5 to PM10, but also as TSP (total suspended particles) and individually as BC – black carbon is vital for effective air quality management and mitigating its multifaceted impacts on our environment and well-being. This paper presents an analysis of PMs emissions divided into the 4 classes presented above, for Romania, between 1990-2021, as given by the national report made according to the National Emission reduction Commitments Directive (NECD), which aims to reduce emissions of the main air pollutants. The emission data are presented by national emission sectors, from different industries to different transport types, and also from agricultural activities, and waste management. Relevant averages and trends are quantified and analysed as a reference for real-time data at city level, such that to assess the local pollution level and data accuracy as compared to the national reports.

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July–August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

Modelling of Deep Street Canyon Air Pollution Chemistry and Transport: A Wintertime Naples Case Study

The impact of urban morphology on air quality, particularly within deep canyons with longer residence times for complex chemical processes, remains insufficiently addressed. A flexible multi-box framework was used to simulate air quality at different canyon heights (3 m and 12 m). This approach incorporated essential parameters, including ventilation rates, background concentrations, photochemical schemes, and reaction coefficients. A field campaign within a deep canyon with an aspect ratio of 3.7, in Naples, Italy was conducted and used for the model evaluation. The model performance demonstrated good agreement, especially at the street level, when employing a realistic light intensity profile and incorporating volatile organic compound (VOC) chemistry. Our findings indicate that peroxyl radical production affects NO2 and O3 levels by up to 9.5% in deep canyons and underscore the significance of vertical distribution (approximately 5% variance) in health assessments and urban air quality strategy development. The model response was sensitive to changes in emissions as expected, but also, somewhat more surprisingly, to background conditions, emphasizing that policies to remove pollution hotspots must include local and broader citywide action. This work advances the understanding of air quality dynamics in deep urban canyons and presents a valuable tool for effective air quality management in intricate urban environments.

Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model.

Air quality has emerged as a critical concern in recent years, with the concentration of PM2.5 recognized as a vital index for assessing it. The accuracy of predicting PM2.5 concentrations holds significant value for effective air quality monitoring and management. In response to this, a combined model comprising CEEMDAN-RLMD-BiLSTM-LEC has been introduced, analyzed, and compared against various other models. The combined decomposition method effectively underlines the fundamental characteristics of the data compared to individual decomposition techniques. Additionally, local error correction (LEC) efficiently addresses the issue of prediction errors induced by excessive disturbances. The empirical results of nine steps indicate that the combined CEEMDAN-RLMD-BiLSTM-LEC model outperforms single prediction models such as RLMD and CEEMDAN, reducing MAE, RMSE, and SAMPE by 36.16%, 28.63%, 45.27% and 16.31%, 6.15%, 37.76%, respectively. Moreover, the inclusion of LEC in the model further diminishes MAE, RMSE, and SMAPE by 20.69%, 7.15%, and 44.65%, respectively, exhibiting commendable performance in generalization experiments. These findings demonstrate that the combined CEEMDAN-RLMD-BiLSTM-LEC model offers high predictive accuracy and robustness, effectively handling noisy data predictions and severe local variations. With its wide applicability, this model emerges as a potent tool for addressing various related challenges in the field.

Physical informed neural network improving the WRF-CHEM results of air pollution using satellite-based remote sensing data

Accurate measurement of air gases concentration are crucial for effective air quality management and minimizing the harm caused by pollution events, as well as providing guidance for healthy travel. We developed develop a physical informed deep learning model that combines a traditional atmospheric chemical transport model and a data-based deep learning model. The model uses multisource observation data to constrain and optimize the results of atmospheric pollution gases concentrations. The accuracy and spatial resolution in central and eastern China (20–45°N, 100–125°E) have been improved compared to the original Weather Research and Forecasting model coupled to Chemistry (WRF-CHEM) results by incorporating constraints from ground-based and satellite observations. Compared with the WRF-CHEM results, our model increases the verification Pearson coefficient (R) of NO2 and CNEMC sites from 0.56 to 0.8, and the RMSE decreases from 25.33 to 15.54 μg cm−3. For O3 results, the model increased the verification R of the CNEMC from 0.57 to 0.76, and the RMSE decreased from 24.55 to 20.22 cm−3. We employed independent MAXDOAS monitoring to authenticate the HCHO results. The R was 0.78, while the validation results for satellite HCHO and MAXDOAS exhibited a R of 0.63 and the validation results for WRF-CHEM and MAXDOAS demonstrated a R of 0.11. Additionally, the RMSE was 6.97E15 mole cm−2, whereas the validation results for satellite HCHO and MAX-DOAS had an RMSE of 7.16E15 mole cm−2. By utilizing satellite remote sensing to effectively capture the spatial distribution of pollutants, and by combining satellite data with other sources of monitoring data and optimizing our models, we have improved the accuracy of our results and have overcome the limitation of cloud coverage. As a result, we are now able to better understand the space-time distribution of pollutants in different regions.

Development of point sampling technology for identifying high-emitting vehicles in narrow and deep street canyons

Side road measurements of fine particle (FP) concentrations were performed in two narrow and deep street canyons in the historic center of Naples using point sampling technology (PS). The FP concentration in the range of 20–103 nm was measured using a condensation particle number counter (CPC) with a 1 Hz frequency. Video recording was used to detect passing vehicles classified into three categories: motorcycles, cars, and light-duty vehicles. The two monitored streets were adjacent, with similar geometrics and orientation but different high- and low-traffic levels. FP concentration time series data were processed to identify concentration peaks generated by the exhausts of passing vehicles. A sensitivity analysis study was carried out to determine how data processing parameters could affect high-emitting vehicle (high emitter) identification. Once the optimum parameters were defined, the contribution of high emitters exhaust emissions to local air quality was assessed. The results showed that PS technology offered viable opportunities for application in narrow street canyons where deploying more common crossroad and top-down monitoring can be troublesome. However, some issues remain to be solved to accurately identify high emitterss in high-traffic cases. PS technology can be useful for developing effective air quality management policies in historical centres.

A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.

Extreme Concentrations of Nitric Oxide Control Daytime Oxidation and Quench Nocturnal Oxidation Chemistry in Delhi during Highly Polluted Episodes.

Delhi, India, suffers from periods of very poor air quality, but little is known about the chemical production of secondary pollutants in this highly polluted environment. During the postmonsoon period in 2018, extremely high nighttime concentrations of NOx (NO and NO2) and volatile organic compounds (VOCs) were observed, with median NOx mixing ratios of ∼200 ppbV (maximum of ∼700 ppbV). A detailed chemical box model constrained to a comprehensive suite of speciated VOC and NOx measurements revealed very low nighttime concentrations of oxidants, NO3, O3, and OH, driven by high nighttime NO concentrations. This results in an atypical NO3 diel profile, not previously reported in other highly polluted urban environments, significantly perturbing nighttime radical oxidation chemistry. Low concentrations of oxidants and high nocturnal primary emissions coupled with a shallow boundary layer led to enhanced early morning photo-oxidation chemistry. This results in a temporal shift in peak O3 concentrations when compared to the premonsoon period (12:00 and 15:00 local time, respectively). This shift will likely have important implications on local air quality, and effective urban air quality management should consider the impacts of nighttime emission sources during the postmonsoon period.

Health Risks of Major Air Pollutants, their Drivers and Mitigation Strategies: A Review

The impact of increasing air pollution on human health and the environment is a major concern worldwide. Exposure to air pollution is one of the leading risk factors and substantially contributes to morbidity and premature mortality. This review paper aims to examine the exposure of major air pollutants (i.e., particulate matter, sulfur dioxide, oxides of nitrogen, carbon monoxide) and its association with respiratory, cardiovascular, reproductive, and genotoxic adverse health outcomes that can cause DNA damage leading to genetic mutations. The study emphasized how a better understanding of source-receptor relationships and exposure assessment methodologies can support effective air quality management planning. Hence, there is a need to augment various exposure indicators (spatial modeling, personal/area monitoring, emphasizing central/rural site measurements, etc.) to generate reliable surrogates for informed decision-making. The critical drivers of anthropogenic interference for air pollution remain urbanization, growing vehicle use, and industrialization. This requires innovative approaches, such as energy-efficient and technologically sustainable solutions to gradually replace conventional fossil fuel from primary energy mix with renewable energy. It holds the key to meet future energy challenges and minimizing air pollution emissions. Further, there is an urgent need to frame effective public policy with graded mitigation actions to reduce the adverse impact of air pollution on human health and the environment.

Development of strategic air quality improvement framework for urban hotspots

The success of any policy intervention depends on its emission reduction potential, ease of enforcement, scale of implementation and socio-economic feasibility. However, all these parameters are not considered in an integrated manner in the air quality management practices followed in Indian cities. The existing policies are at the city-scale which turn out to be ineffective in addressing extreme air pollution problems at hotspot locations within a city. The present study is an attempt to develop a coherent framework for effective air quality management at identified air pollution hotspot. The study methodology includes sources information, data collection, emission inventorization, control scenario simulations using dispersion modeling, socio-economic assessment, ease of enforcement and ranking of the actions for implementation. The suggested process has been applied at an urban industrial area, (also identified PM2.5-APH) in Delhi city. Estimation of micro-level emission inventory in the surrounding area (2 km × 2 km) of the PM2.5-APH suggests highest anthropogenic emissions from road dust (45–56%) followed by multiple type of industries (18–21%). The maximum reduction in PM2.5 concentrations can be achieved by allowing industrial operations at full capacity during day-time only (24–40%) and road dust cleaning by mechanized vacuum machines (30–31%). An integrated criteria is developed for prioritization/ranking of control actions. The most effective actions are found to be i) road dust cleaning by mechanized vacuum machine, ii) operation of industries with full capacity during day time only, iii) installation of cyclone dust collector in industrial stacks and iv) increase in stack height by 50%. This strategic framework can aid air quality managers in selection of the most suitable and appropriate policy interventions for an air pollution hotspot. Further, it can be replicated at other hotspot to prioritize the control actions for effective air quality management in any city.

Conceptualising air quality management instruments in South Africa

South Africa has developed a plethora of air quality management instruments as a means to address and mitigate air quality challenges. However, no holistic conceptualised understanding of these instruments exists to critically inform air quality management and governance. The aim of this paper is to identify existing air quality management instruments and conceptualise them in terms of three broad environmental management approaches, namely Command and Control-based (CaC), Fiscal-based and Civil-based. This allows for a critical understanding of the overall air quality governance framework in South Africa. A literature review methodology was followed to identify the different instruments. The research results suggest an over reliance on CaC, high levels of hybridisation, high level of complexity and an overall lack of synergy between instruments. The research notes that even amidst the plethora of instruments South Africa continues to face serious air quality challenges. We hope that the conceptualisation provided in this paper provides a basis towards a more detailed analysis of the strengths, weaknesses, and performance of different approaches and instruments to ensure more effective air quality governance and management in South Africa.

Assessment of Environmental Policies and Institutions for Effective Air Quality Management in Nepal

Air pollution is a serious environmental threat in major cities of Nepal. The major emission source of air pollutants is vehicular emission, resuspension of street dust litter during dry climatic conditions, black smoke plumes from brick kilns, and refuse burning. Effective plans and policies are required for the effective management and monitoring of air pollution. This study aims to review environmental policies and plans prevalent in Nepal and assess their effectiveness in controlling air pollution. The study is based on the literature review and a questionnaire survey using Najam's 5C framework for policy analysis. Although several national policies, plans, laws and standards have been formulated and international agreements have been ratified for the management of air pollution, separate policy dedicated to air pollution management has not been formulated in Nepal. According to Najam's framework, the content, context, and commitment of the overall policies, plans, and agreements for managing air quality in Nepal were assessed as effective. However, the capacity of the implementor, client support, and management coordination was found ineffective. This study recommends that a separate policy on air quality management is required, while the capacities of stakeholders and coordination mechanisms should be strengthened to improve the country's air quality.