Evaluation Of Air Quality Research Articles

Geospatial Evaluation of Ambient Air Quality in Akure South Local Government Area, Ondo State, Nigeria

The study evaluates the ambient air quality in Akure South Local Government Area, Ondo State, Nigeria, focusing on particulate matter (PM2.5 and PM10) and its health implications on residents. Primary data were collected using Purple Air Monitor devices at 28 ground stations and structured questionnaires, base maps. The study utilized QGIS for mapping, SPSS for statistical analysis and WHO’s AirQ software for health risk assessment. Findings indicated exceedances of WHO and USEPA recommended limits for PM2.5 and PM10, with areas near major highways showing elevated PM levels. The health risk assessment revealed potential increases in hospitalizations and mortality due to air pollution. This research contributes uniquely by applying geospatial evaluation and health risk assessment tools for comprehensive air quality analysis. It highlights the urgent need for effective air quality management strategies, including the deployment of air quality monitoring stations and public education campaigns. The study provides crucial insights for policy makers, emphasizing the importance of mitigating air pollution’s adverse effects on urban settings. Received: 15 September 2024 / Accepted: 8 November 2024 / Published: 20 November 2024

Assessing the impact of coal furnace activities on air quality: A comprehensive spatial and temporal analysis

Air pollution is a critical threat to public health and environmental systems, primarily driven by industrial activities such as coal furnace operations. This study uses advanced spatial analysis techniques, including kriging and Principal Component Analysis (PCA), to assess the impact of these activities on air quality in a designated study area. The Air Quality Index (AQI) is also employed to evaluate air quality in this research. Additionally, five key variables—number of households, coal furnaces, average income, average cost, and AQI—are normalized using the MinMaxScaler for comparative analysis. Also, the results indicate that the southern and western regions of the study area have the highest pollutant concentrations, correlating with high AQI values and the density of coal furnaces and industrial activities. The PCA results show that urban areas are mainly affected by CO2 and SO2, significantly influencing the AQI, while agricultural land and other types of land (rangeland, forest, dryland, barren land) show varying dominant pollutants. Agricultural lands were found to be primarily impacted by SO2, CO2, and CO. In addition, a temporal analysis reveals a 10% annual increase in coal furnace operations from 2003 to 2024, contributing to the rise in pollution levels. The AQI optimization results also indicate a strong negative correlation between the number of coal furnaces and AQI, suggesting that reducing the number of coal furnaces can lower air pollution levels. These findings highlight the significant role of industrial emissions in worsening air quality and emphasize the need for cleaner technologies, reduced coal furnace usage, and stronger regulatory measures.

Short-term and long-term exposure to particles and their consequences in Poldokhtar City (Iran)

In Iran's biggest towns, deaths and hospitalizations from respiratory and cardiovascular diseases have a strong association with PM2.5 pollution concentrations. The WHO recommends assessing the health impacts using the Air Quality and Health Evaluation approach (Air Q 2.2.3). Data of particulates on both clear, dusty days have been provided by the Meteorology Office for Lorestan. Results indicated that in terms of mean AQI, May (162.46), July (121.7), and April (110.23) are the most polluted months in Poldokhtar city in 2022. May (16 days), July (6 days), March (5 days), and April (4 days) are the most contaminated months of the total number of polluted days. The days having the highest amounts of pollution in terms of the daily mean AQI are May 17th (407), April 10th (402), May 24th (393), July 31st (351), and April 18th (341). The maps extracted from HYSPLIT showed that the origin of the dust entering the city of Poldokhtar is the arid and semi-arid regions of Saudi Arabia, Egypt, Kuwait, and Turkey. May shows the maximum amount of pollution in comparison to other months, as shown by the mean AQI of 162.46. Furthermore, with an AQI score of 407 on May 17, it is assumed to be the most polluted day of the year. Hospitalized people who had respiratory diseases were most severely impacted by the short-term adverse effects of fine dust inhalation.

A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM2.5) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a Depthwise-Partial Convolutional Neural Network (DW-PCNN) to fill gaps between surface PM2.5 stations, utilizing Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a Deep Convolutional Neural Network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM2.5 concentrations accurately, evidenced by a Pearson’s correlation coefficient (R) of 0.92 and an Index of Agreement (IOA) of 0.96 in ten-fold cross-validation. We also introduce a grid-based method for calculating PM2.5 Design Values (DV), providing a continuous spatial representation of PM2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model's ability to provide spatiotemporally consistent, gap-free PM2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.

Analysing Indoor Air Pollution: A Study on Pollutant Levels and Air Quality Assessment in Social Housing Properties

Amid growing concerns over Indoor Air Quality (IAQ), this paper presents a comprehensive analysis of pollutants to assess the environmental health in a few Case Study Buildings (CSBs). The main objectives were to evaluate the levels of key Indoor Air Pollutants (IAPs) (PM10, PM2.5, CO, CO2, TVOCs) and relative humidity across multiple social housing properties and to analyze how occupant behaviormay affect IAQ. Utilizing quantitative measurements, the study investigates the range and exposure of the building occupants to IAPs. The methodology involves the systematic measurement of IAPs through the installation of data loggers, focusing on pollutant levels to evaluate air quality across multiple CSBs. Findings show fluctuation in pollutant levels, with some IAPs demonstrating consistently within the acceptable range while others exhibit sporadic spikes in pollutant concentrations. The results indicate that significant attention should be given to PM10, CO, and VOCs. This study underscores the importance of continuous monitoring and targeted interventions to mitigate poor IAQ and ensure healthier indoor environments for building occupants. Further research and implementation of effective measures are imperative to safeguard public health and well-being.

A comprehensive study on the validation and application of multi-lognormal distribution models for atmospheric particles

The particle number size distribution (PNSD) is crucial for evaluating air quality and mitigating environmental pollution, as particles of different sizes have diverse effects on human health and climate. However, obtaining a comprehensive understanding of PNSD is challenging due to its inherent complexities and variability. Multi-lognormal distribution models are employed to fit PNSD, as seen in climate models, but discrepancies between model fits and observed PNSD persist. This study adopts hourly data from 2017 to 2020 across eight monitoring sites in diverse environments—rural, urban, mountainous, and polar, and compares the observed PNDS with those simulated by multi-lognormal distribution models. The results demonstrated that the model generally achieved a high correlation with observed PNSD data (r2 > 0.75), effectively capturing key characteristics of nucleation and Aitken mode particles. However, the model had a tendency to overestimate the total number concentration by approximately 1.09 times, particularly noticeable under conditions of high concentrations of smaller particles. The model successfully represented prevalent bimodal size distribution patterns in urban areas with high ultrafine particle concentrations, though its performance was slightly less accurate in scenarios involving trimodal distributions. Despite these strong correlations and the model's ability to reflect diurnal and seasonal variations, which suggests its broad applicability and utility, there were notable limitations on smaller time scales and in specific particle size ranges. These limitations were particularly evident in capturing detailed phenomena relevant to new particle formation events, indicating areas where model refinement is necessary. The results highlighted the importance of investigating discrepancies between model predictions and actual observations, which is crucial for refining climate models that utilize PNDS. The uniform comparison facilitated a detailed exploration of particle properties from model results, offering deeper insights into aerosol behavior and its environmental impacts.

Comprehensive evaluation of air quality: incense burning and night market emissions in Kaohsiung, Taiwan, using the ISCST3 air quality model

Comprehensive evaluation of air quality: incense burning and night market emissions in Kaohsiung, Taiwan, using the ISCST3 air quality model

Evaluation of Air Quality (PM10 and SO2) Parameters: Example of Central Anatolia Region

Canlı yaşamını, çevresel dengeyi önemli derecede etkileyen ve küresel iklim değişikliğinin ana nedenlerinden biri olan hava kirliliği günümüzde önemli bir çevre sorunudur. Hava kirliliği etkilerinin dünya çapında seviyelere erişmesiyle birlikte hava kirliliğine sebebiyet veren kaynaklar da daha çok araştırmaya konu edilmektedir. Hızla artan dünya nüfusuyla paralel olarak kaynakların enerji için tüketimi de çoğalmıştır. Nüfus artışı ve sanayi alanının ihtiyaçları sebebiyle hava kirliliği günden güne artmakta ve bütün doğa (canlı ve cansız) bundan olumsuz etki görmektedir. Türkiye’nin havasındaki kirlilik, yüksek seviyede ısınma, sanayi ve ulaşım kaynaklıdır. Bununla birlikte yan etkenlerin de etkisiyle kış döneminde kirliliğin daha da arttığı bilinmektedir. Bu çalışmada İç Anadolu bölgesinde bulunan Niğde, Nevşehir, Konya, Kayseri ve Karaman illeri olmak üzere 5 ilin 2017-2021 yılları arasındaki PM10 ve SO2 konsantrasyonları değerlendirilmiştir. Ölçüm periyodu boyunca elde edilen yıllık PM10 konsantrasyon değerleri 23-88 µg/m3, SO2 konsantrasyon değerleri ise 7-31 µg/m3 aralıklarında değişiklik göstermektedir. Sonuçlar değerlendirildiğinde, beş ilin SO2 değerlerinin sınır değerlerin altında olduğu fakat PM10 değerlerinin sınır değerleri aştığı görülmektedir. PM10 seviyesinin özellikle kış aylarında, hava kirliliğinde ciddi payı olduğu tespit edilmiştir.

INDOOR AIR QUALITY MONITORING IN DWELLINGS: CASE STUDIES IN MADRID, SPAIN

ABSTRACT The quality of the indoor environment has evolved into a vital parameter for dwellings due to the increasing number of home-based jobs, with more people spending most of their time at home. Generally, the design and evaluation of indoor air quality in buildings depend on guidelines and recommendations from national and international standards. In a building stock in which mechanical ventilation is not mainstream, it is necessary to evaluate the natural ventilation habits and their impact on indoor air quality. This article describes a methodology to assess indoor air quality using four case studies in which habits and occupancy are representative samples of dwellings in Madrid, Spain. Temperature, relative humidity, formaldehyde, particle matter (PM), and carbon dioxide (CO2) concentrations have been measured over several months. The analysis of these values leads to conclusions that each pollutant has a different source. For example, Formaldehyde concentration depends mostly on indoor finishes and materials; indoor activities impact PM concentrations. The results showed high formaldehyde concentrations in the case study decorated with oil paintings, whereas bedrooms with insufficient ventilation showed consistently high CO2 concentrations. Finally, well-ventilated rooms showed high PM concentrations due to the smoking habits and pets that live with the family.

APD-BayTM: Jakarta Air Quality Index Prediction using Bayesian Optimized LSTM

The Air Quality Index (AQI) is a metric for evaluating air quality in a region. Jakarta holds the fifth position globally in terms of air pollution. Several studies have been performed to forecast pollution levels in Jakarta. However, existing studies exhibit limitations such as outdated datasets, lack of data normalization, absence of machine learning parameter setting, neglect of k-fold cross-validation, and a failure to incorporate deep learning algorithms for pollution detection. This study introduces an air quality detection system called APD-BayTM to address these issues. This proposed system leverages Long Short-Term Memory (LSTM) and uses Bayesian Optimization (BO) to enhance the performance of air pollution detection. The methodology used in this research involves four key steps: data preprocessing, LSTM model development, hyperparameter tuning through BO, and performance assessment using 5-fold cross-validation. APD-BayTM exhibits robust performance that is comparable to previous research outcomes. The LSTM model in APD-BayTM on the training dataset achieved average precision, recall, F1 score, and accuracy values of 93.29%, 91.41%, 91.89%, and 95.90%, respectively. These metrics improved on the test dataset, reaching 97.44%, 99.71%, 98.52%, and 99.34%, respectively. These findings show the robustness of APD-BayTM across datasets of varying sizes, encompassing both large and small datasets.

Evaluation of Urban Air Quality Based on Entropy Weight Method

Evaluation of Urban Air Quality Based on Entropy Weight Method

Predicting air quality index and fine particulate matter levels in Bagdad city using advanced machine learning and deep learning techniques

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

Estimating Chemical Concentrations of Dust PM2.5 in Iraq: A Climatic Perspective Using Polynomial Model and Remote Sensing Technology

Air pollution and climate change are interrelated issues, with air pollution levels in Iraq currently exceeding World Health Organization standards. This study aimed to evaluate air quality in Iraq by utilizing climatic data, such as temperature, humidity, and gaseous pollutants for assessing the health effects based on processed and estimated data. The research was conducted between August and November 2020, using remotely sensed images and geographical information techniques. Two methods; Geographic Information Systems GIS-based multiple regression and a polynomial model, were employed to estimate PM2.5 levels in the study area. The results showed a significant influence of climatic variables on air pollution in Iraq, with varying effects on PM2.5 estimation. The health impact ranged from good to unhealthy, with most provinces experiencing poor air quality. Southern parts of Iraq exhibited PM2.5 levels surpassing the healthy threshold. The predictive linear and polynomial model's accuracy was assessed through regression, yielding high correlation coefficients (R2 ) of 0.89, 0.95, 0.98, and 0.96 for August to November, respectively. While model validation accuracy ranged between 85–94 %. The study emphasizes the vital role of climate data in understanding the dispersion of air pollutants and their significant impacts on the environment. Addressing air pollution and climate change, as per the SGS-13 "Climate Action", are interconnected and require comprehensive strategies for mitigation.

Green Design Parameters in High Rise Building in Hot Humid Climate

Abstract: The term "green building" is used to describe buildings that are designed, constructed and operated, to have a minimum impact on the environment, both indoor and outdoor. Most discussions of green buildings refer to the importance of providing an acceptable, if Not exceptional, indoor environment for the building occupants. However, these discussions of indoor environment quality have Not included many specific recommendations or criteria for building design, construction, or operation. Building projects described as green building demonstrations often make reference to indoor air quality, but these references are often general and qualitative. In addition, rating systems that have been developed to assess the "greenness" of a building are based largely on design features and are Not particularly specific with respect to indoor air quality. This Project reviews the features of indoor air quality that are considered in green building discussions, demonstration projects, and rating systems. These green building features are discussed in terms of their completeness and specificity, and are compared to other guidance on building design, construction, and operation for good indoor air quality. A case study of indoor air quality performance in a green building is presented. This study includes adscription of the indoor air quality features of the building and the results of a short-term indoor air quality evaluation of the building involving ventilation and contaminant concentration measurements

A Multidisciplinary Approach to the Evaluation of Air Quality and Thermo-Hygrometric Conditions for the Conservation of Heritage Manuscripts and Printed Materials in Historic Buildings: A Case Study of the Sala del Dottorato of the University of Perugia as a Model for Heritage Preservation and Occupants’ Comfort

The Sala del Dottorato (Hall of Graduates) is a magnificent library in the University of Perugia which plays the double role of providing optimal conservation of valuable books and manuscripts while also hosting important events. This double role is closely connected to contrasting indoor microclimatic conditions. This paper presents the results of a multidisciplinary study, begun in 2019, which investigates optimal conditions for the conservation of volumes by monitoring thermo-hygrometric and air quality parameters. The study describes the current conditions of the Hall (in terms of air temperature, relative humidity and concentration of CO2), highlighting critical aspects, defining strategies for their mitigation and control, and outlining future developments. Improvement measures relate to the installation of a permanent monitoring system with alarm settings and data storage, technical interventions on the windows, and the restoration of several volumes. The paper shows the importance of monitoring as an instrument of control in real time and provides guidelines for management to be implemented according to indoor microclimatic conditions.

Comparative analysis of energy, exergy, emission, and sustainability aspects of third generation microalgae biodiesels in a diesel engine

Third-generation biodiesel produced from various microalgae species is being researched for its production, conversion, compatibility, sustainability, and environment-friendly behaviour for the diesel engine as a replacement of conventional diesel fuel. The present work is conducted with the objective of analysing a clean, green, and sustainable microalgae biodiesel for energy, exergy, emission, and sustainability assessment in a diesel engine system, along with the evaluation of air quality in terms of emission analysis for assessing its impact on climate. The fuel samples from diesel and the three microalgae species, viz., Chlorella emersonii, Euglena sanguinea, and Spirulina, are designated as DF100, CE100, ES100, and SP100, respectively. The engine behaviour is assessed and compared at 1500 rpm under 17.5 compression ratio with 25–100 % engine load. DF100 achieves the maximum energy and energy efficiency of 32.76 % and 30.74 %, respectively, and a varied range of 27.32–30.37 % and 25.28–28.10 % with microalgae biodiesels. The air quality is improved with the reduction of PM emissions by 15.82–27.09 % at the cost of improved nitrogen oxide (NOx) emissions by 11.03–15.55 %. The sustainability index is highest for DF100 (1.247–1.444), and among biodiesel fuel samples, it is highest for SP100 (1.233–1.391) at all engine loads. These results point to the possibility of microalgae-derived biodiesel blends as feasible diesel fuel substitutes, providing environmentally friendly and effective solutions for internal combustion engines.

Air Quality Index Assessment Of An Educational Organization

Abstract Uncontaminated and fresh air is an elementary prerequisite of every living being. But, in this day and age, atmosphere contamination-is certainly the most pervasive kind of environmental-degradation. In the ambience, there are several pollutants which have widespread causing contamination in the air. Pollutants of chief concern include gaseous pollutants like carbon monoxide, ozone, oxides of sulphur and nitrogen, lead etc. These are primary pollutants. Secondary pollutants are particulate matters of different types ranging from fine to high. Every single one of these pollutants can cause a severe menace to human-health. In the current research, sensor-based equipments have been used to monitor and measure the concentration of various pollutants in the air. In the present research work, the effect of different pollutants like PM2.5, PM10, NO2 C02, TVOC, H-CHO, Temperature, Humidity and Noise on air pollution at five stations in the education institute namely RCOEM campus is studied and presented. It had been observed that air pollutants like PM2.5, PM10, and NO2 primarily affected the air quality of RCOEM campus. So, these pollutants are mainly considered to monitor and evaluate air quality of RCOEM campus. After evaluation, air quality index of RCOEM campus was found to be around 94.5. It is within the range of 51-100. So, its falls in the range of satisfactory as per standard norms of central pollution control board and state pollution control board in terms of quality of the air and thereby its contamination.

Enhancing outlier detection in air quality index data using a stacked machine learning model

AbstractThe air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far‐reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter (g/m). The dataset is then preprocessed by cleaning and normalizing it before using K‐means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K‐means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K‐nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1‐score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1‐score of 0.99, 0.99, 0.97, and 0.99, respectively.

Evaluation of Ambient Air Quality Levels at Various Locations within Lead City University, Ibadan

Universities are big metropolitan institutions with sizable populations of students, employees, and visitors. However, university settings can contribute to air pollution, with diverse activities such as lab work, cooking in dorms, and vehicle traffic, among others, causing interior and ambient air pollution. It is impossible to overestimate how much air pollution affects the health and happiness of students. Based on this, evaluation of ambient air quality (i.e., temperature, relative humidity (RH), CO2, and particulate matter (PM2.5)) at various locations within Lead City University, Ibadan, is essential. Ambient concentrations of carbon monoxide (CO), carbon dioxide (CO2), relative humidity (RH), temperature (TEMP), and particulate matter (PM) were recorded from 15 locations across Lead City University over a two-week period. Morning temperatures in all the locations measured ranged from 23.7°C to 29.2°C, while afternoon temperatures fluctuated more significantly, with the lowest recorded at 27.2°C and the highest at a notably warmer 35.8°C in all the locations measured. The morning RH levels ranged from 63.8% to 74.7%, while afternoon RH values ranged from 58.2% to 63.4%. The findings also show that afternoon CO2 levels range from 468.5 ppm to 971.6 ppm, with Location 13 having an unusually high average. Morning CO levels ranged from 4.1 µg/m³ to 49 µg/m³, with location 13 showing an outlier at 184.2 µg/m³. CO2 and CO levels are mostly within acceptable ranges. Conclusively, variability in these parameters has implications for human health; therefore, adequate ventilation and pollution control measures are thereby recommended for the university management in order to improve indoor air quality.

Air quality and public health effects of dairy digesters in California

The dairy industry in California emits large amounts of methane (CH4) that contributes significantly to the state's overall Greenhouse Gas (GHG) budget. Reducing CH4 emissions has become a key priority for dairy farms in support of California's GHG reduction goals. Anaerobic digesters designed to capture CH4 from animal manure present a practical option for reducing CH4 emissions, but a comprehensive evaluation of the local air quality impacts of this technology has not been previously undertaken. The simplest digester configurations decrease local emissions of volatile organic compounds (VOCs) but increase emissions of oxides of nitrogen (NOx), potentially changing local air quality. Here, we evaluate the air quality implications of widespread digester adoption in the year 2050 across the San Joaquin Valley (SJV) in central California, which is home to the highest concentration of dairy farms in the state. Changes to concentrations of air pollutants including ozone (O3), airborne particulate matter with diameter smaller than 2.5 μm (PM2.5), and various PM2.5 chemical components are predicted using the UCD/CIT chemical transport model at 4 km resolution. Dairy digester adoption is evaluated within two regional energy scenarios, including a business as usual (BAU) scenario and an 80% greenhouse gas reduction (GHGAi) scenario, to consider potential changes to the chemical regime that governs formation of secondary air pollution. Concentrations are evaluated across 32 randomly selected weeks over a 10-year period from the year 2046–2055 to establish a long-term average impact in the presence of El Niño Southern Oscillation (ENSO) variability. Regional weather patterns are downscaled from Global Climate Model simulations under the RCP8.5 global scenario. The results indicate that baseline dairy emissions make minor contributions to air pollutant concentrations in 2050. Under a worst-case scenario for digester adoption, PM2.5 concentrations would increase by 0.06 μg/m3 (current standard = 9 μg/m3), and maximum daily 8-h average (MDA8) O3 would change by −1.0 ppb to +0.2 ppb depending on the surrounding regional energy scenario (current standard = 70 ppb). A health impact analysis shows that the widespread use of dairy digesters would result in fewer than 0.1 additional deaths per 100,000 people due to changing air pollution. For comparison, this level of mortality change is more than 100 times smaller than the risk posed by seasonal flu. Further, Environmental Justice analysis indicates that the implementation of digesters will not influence the exposure disparities among different racial groups in either the SJV or the surrounding San Francisco Bay & Sacramento area. These findings suggest that dairy digesters can be widely adopted in central California to reduce GHG emissions with minimal effect on regional air quality and public health.