Potential Air Quality Research Articles

Particulate Matter Production from Prescribed Burns in the Chicagoland Area

Prescribed burns are a land management tool currently used to aid in fire mitigation and to promote desired plant species and reduce undesired species, which are often invasive species. Currently, there is a public stigma surrounding the negative effects of prescribed burns, which impacts their use near residential areas. There are also particular challenges of conducting prescribed burns in urban and suburban areas. A better understanding of these relatively small-scale prescribed burns and their impact on atmospheric chemistry and air quality can allow for better communication with the public about their positive impacts, as well as for the acknowledgement and quantification of their drawbacks. This study considers the particulate matter concentrations proximate to prescribed fires being conducted in the greater Chicago metropolitan area. We deployed low-cost sensors in the spring and fall of 2022 at four sites. Concentrations of particulate matter exceeded accepted human health exposure limits at locations that burn crews would experience, but levels quickly returned to baseline after the high-intensity phase of the burn. With the ever-expanding wildland–urban interface and increased efforts in ecological restoration, understanding the particulate matter emissions of prescribed fires conducted near populated areas can provide more data regarding air quality impacts. These impacts should be weighed against the ecological benefits of prescribed burns and potential air quality impacts from uncontrolled burns from ecosystems that are not treated with prescribed burns. Measuring air quality impacts can also inform fire management practices with the goal of reducing future emissions. With the very present effects of climate change, understanding baselines regarding prescribed fires can better equip future research and fire professionals in fighting fire with fire.

Potential environmental impact of the chlorine-containing disinfectants usage during the COVID-19

Chlorine radicals (Cl) play a crucial role in atmospheric chemistry. The COVID-19 (coronavirus disease 2019) pandemic significantly increased the use of chlorine-containing disinfectants in China, leading to enhanced emissions of chlorine gas (Cl2) and hypochlorous acid (HOCl). In this study, we use the Community Multiscale Air Quality (CMAQ) model with updated chlorine chemistry to evaluate the potential air quality impact of chlorine emissions from disinfectant usage during February 2020, a month with a large outbreak of COVID-19 in mainland China. Results indicate that during the pandemic, there was a sharp increase of reactive chlorine emissions, with Cl2 and HOCl emissions reaching 773.9 t and 5913.1 t, making 3 times increase. The emissions of chlorine enhanced atmospheric oxidation capacity (AOC) through the oxidation of VOC by Cl and OH, with the average enhancement in Shanghai, Beijing and Wuhan areas reaching 4.6% ± 1.8%, 10.3% ± 6.6% and 7.4% ± 4.6%, respectively. Consequently, the use of chlorine-containing disinfectants may have contributed to observed increases in the monthly average of the maximum daily 8-h average ozone (MDA8 O3) and fine particulate matter (PM2.5) concentrations by up to 1.5 ppbv (5%) and 1.7 μg/m3 (1%), respectively. Especially, the maximum hourly increase values of O3 and PM2.5 concentrations were 4.8 ppbv and 11.4 μg/m³, 2.5 ppbv and 4.5 μg/m³ in Beijing and Wuhan, respectively. These results indicate that chlorine emissions from the widespread use of disinfectants have had a significant impact on air quality in certain regions (Beijing and Wuhan) and during specific periods (9:00∼12:00).

Detection of anomalous Nitrogen Dioxide concentration of Ankara: a Reconstruction-based approach

Air quality significantly impacts human health, particularly in urban areas, leading to global morbidity and mortality. Elevated air pollutant levels pose health risks, emphasizing the need for timely monitoring and detection. This study adopts an innovative approach to identify anomalies of daily NO2 concentration levels in a district of Ankara, Turkey. Leveraging both traditional statistical approaches and state-of-the-art techniques, the research aims to provide real-time alerts. Employing a multivariate strategy, the study generates new features based on historical and current data, and incorporates periodic variables, as well. Among the methods explored, Variational Autoencoder emerges as noteworthy, exhibiting superior performance with %98 recall, %82 precision and %0.12 false alarm rate. This approach not only demonstrates a high true positive rate, enhancing its efficacy in anomaly detection but also effectively mitigates false alarms, preventing alert fatigue. By using advanced methodologies with a focus on NO2 levels, the study contributes to proactive measures for public health, enabling prompt responses to potential air quality issues.

Potential Pollutants and Indoor Air Quality Variables Association towards Implementation of the Breathing Architecture Concept: A Review

Indoor air quality is essential in improving indoor well-being since most people spend their time there. The breathing Architecture concept promotes a strategy for designing indoor space to maintain the indoor air quality. This concept has various challenges, such as indoor pollutants determining the indoor environment to be healthy and decent. This study aims to identify the relationship between IAQ variables related to Breathing Architecture and air pollutants based on secondary sources of past and existing research ten years ago. The review identified 16 articles collected by selecting several relevant keywords in ScienceDirect and screening the title and abstract. Content analysis was conducted to analyze the relationship between IAQ variables and pollutants and their potential association with the breathing architecture concept. The results show that air exchange rate, volume of spaces, relative humidity, and indoor-outdoor temperature are the most potential variables and have a possible relationship while implementing the BA concept. Using these variables, the optimal condition might be perceived since the possibility of lower pollutant contamination might be lower, and the purpose of breathing architecture might be obtained. However, the relationship between other variables and pollutants requires future studies since it is undescribed or may impact other variables.

Assessment of the energy-saving and environment effects of China's gasoline vehicle withdrawal under the impact of geopolitical risks

With the increasing number of geopolitical events, energy and economy have been affected in all regions of the world. Uncertain geopolitical risks have led to the instability of the energy supply chain, thus affecting the future planning of fuel vehicles. Accordingly, the environmental effects of “Banning gasoline vehicle sales policy” (BGVSP) will also be affected by geopolitical risks. Therefore, using life-cycle assessment and the Lotka–Volterra model, this study evaluated the environmental effects of BGVSP in terms of energy consumption, carbon emissions, human toxic potential (HTP), global warming potential (GWP), acidification potential (AP), air quality potential (AQP), and photochemical ozone creation potential (POCP). We further analyzed the effects of differences in policy timing on the energy-conservation and emission-reduction benefits of BGVSP under multiple scenarios. The results show that the implementation of BGVSP in 2025 reduces the energy consumption, carbon emissions, GWP and POCP of the Chinese passenger vehicle market by 17.2%, 18.6%, 17.9% and 46.2%, respectively. However, it may also enhance HTP, AQP, and AP impact by 5.3%, 5.6%, and 18.2%, respectively. Meanwhile, lightweight automobile development could also help reduce energy consumption and carbon emissions in the passenger vehicle market. In this situation, the environmental effects of BGVSP implementation are superimposed. Moreover, the emission-reduction effect of new-energy vehicles is linked to the energy structure in terms of the whole life cycle; thus, the environmental effect of BGVSP in the context of energy cleaning is “super additive”. Regarding environmental effects, 2030 would be the optimal time for China to implement BGVSP as it transitions to clean energy.

Impacts of ground-level ozone on sugarcane production

Sugarcane is a vital commodity crop often grown in (sub)tropical regions which have been experiencing a recent deterioration in air quality. Unlike for other commodity crops, the risk of air pollution, specifically ozone (O3), to this C4 crop has not yet been quantified. Yet, recent work has highlighted both the potential risks of O3 to C4 bioenergy crops, and the emergence of O3 exposure across the tropics as a vital factor determining global food security. Given the large extent, and planned expansion of sugarcane production in places like Brazil to meet global demand for biofuels, there is a pressing need to characterize the risk of O3 to the industry. In this study, we sought to a) derive sugarcane O3 dose-response functions across a range of realistic O3 exposure and b) model the implications of this across a globally important production area. We found a significant impact of O3 on biomass allocation (especially to leaves) and production across a range of sugarcane genotypes, including two commercially relevant varieties (e.g. CTC4, Q240). Using these data, we calculated dose-response functions for sugarcane and combined them with hourly O3 exposure across south-central Brazil derived from the UK Earth System Model (UKESM1) to simulate the current regional impact of O3 on sugarcane production using a dynamic global vegetation model (JULES vn 5.6). We found that between 5.6 % and 18.3 % of total crop productivity is likely lost across the region due to the direct impacts of current O3 exposure. However, impacts depended critically on the substantial differences in O3 susceptibility observed among sugarcane genotypes and how these were implemented in the model. Our work highlights not only the urgent need to fully elucidate the impacts of O3 in this important bioenergetic crop, but the potential implications air quality may have upon tropical food production more generally.

A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.

Port-of-Entry Simulation Model for Potential Wait Time Reduction and Air Quality Improvement: A Case Study at the Gateway International Bridge in Brownsville, Texas, USA

The mathematical study known as queueing theory has recently become a major point of interest for many government agencies and private companies for increasing efficiency. One such application is vehicle queueing at an international port-of-entry (POE). When queueing, fumes from idling vehicles negatively affect the overall health and well-being of the community, especially the U.S. Customs and Border Protection (CBP) agents that work at the POEs. As such, there is a need to analyze and optimize the border crossing queuing operations to minimize wait times and number of vehicles in the queue and, thus, reduce the vehicle emissions. For this research, the U.S.–Mexico POE located at The Gateway International Bridge in Brownsville, Texas, is used as a case study. Due to data privacy concerns, the hourly wait times for vehicles arriving at the border had to be extracted manually each day using a live wait time tracker online. The data extraction was performed for the month of March 2022. Using these wait times, a queueing simulation software, SIMIO, was used to develop an interactive simulation model and calibrate the service rates. The output from the SIMIO model was then used to develop an artificial neural network (ANN) to predict hourly particulate matter content with an R2 of 0.402. From the ANN, a predictive equation has been developed, which may be used by CBP to make operational decisions and improve the overall efficiency of this POE. Thus, lowering the average wait times and the emissions from idling vehicles in the queue.

Incorporation of Green Infrastructure on Road Tunnel Ventilation Stacks: Potential Ambient Air Quality Improvement

Vehicular tunnels are an important part of transportation infrastructure, though there are concerns about air quality due to the concentrated release of polluted air at roadway portals and tunnel ventilation stacks. Urban green infrastructure offers opportunities as a nature-based solution to mitigate urban particulate matter pollution. Green walls have advantages over other types of urban green infrastructure, since they can be potentially applied to large vertical surfaces available within cities and since they can be retrofitted onto already built structures. However, uptake of green wall technology has been limited on infrastructure projects, let alone the integration of green walls and roadway portals and tunnel ventilation stacks, which represents the first of its type worldwide. Therefore, this study aims to describe the integration of green wall technologies and a tunnel ventilation facility, and models the potential ambient air quality improvement the green wall plants have through PM2.5 dry deposition. It was found that the project would increase ambient PM2.5 to a concentration of 13.6 ug/m3, and that the green walls would have the capacity to remove significant amounts of PM2.5, from not only the ambient air pollutant increase as a result of the ventilation outlets, but also the background and surrounding surface road contributions. Differences in the rate of pollutant removed per stack by the green walls were attributed mostly to the varying ratios of plants installed. The findings indicate the air quality benefits of green wall integration on major infrastructure projects, which will assist in the overall sustainability appraisal of large infrastructure projects which need to balance the needs of population growth with environmental and human health.

Footprints of COVID-19 on Pollution in Southern Spain

Background: Many annual deaths in Spain could be avoided if pollution levels were reduced. Every year, several municipalities in the Community of Andalusia, located in southern Spain, exceed the acceptable levels of atmospheric pollution. In this sense, the evolution of primary air pollutants during the March–June 2020 lockdown can be taken as reliable evidence to analyze the effectiveness of potential air quality regulations. Data and Method: Using a multivariate linear regression model, this paper assesses the levels of NO2, O3, and PM10 in Andalusia within the 2017–2020 period, relating these representative indices of air quality with lockdown stages during the pandemic and considering control variables such as climatology, weekends, or the intrusion of Saharan dust. To reveal patterns at a local level between geographic zones, a spatial analysis was performed. Results: The results show that the COVID-19 lockdown had a heterogeneous effect on the analyzed pollutants within Andalusia’s geographical regions. In general terms, NO2 and PM10 concentrations decreased in the main metropolitan areas and the industrial districts of Huelva and the Strait of Gibraltar. At the same time, O3 levels rose in high-temperature regions of Cordoba and Malaga.

Air quality impacts of electric vehicle adoption in California

The transition of onroad vehicles from internal combustion engine vehicles to electric vehicles (EVs) has been promoted in the United States at federal and state levels to reduce emissions of conventional air pollutants and CO2. One state level effort is a plan from California to mandate that all new passenger vehicle sales are EVs by 2035. Using the Community Multiscale Air Quality (CMAQ) model, we analyze the potential air quality impacts of having varying amounts of EVs in the California fleet for a historical (2016) and a future (2028) year, with emphasis on ozone (O3) and fine particulate matter (PM2.5). We find that for both PM2.5 and O3, reductions in concentrations scale approximately linearly with increasing EV fleet turnover. Improvements in air quality are widespread throughout the state, with the largest improvements occurring near Los Angeles and throughout the Central Valley. With full electrification of onroad vehicle fleets, the statewide population-weighted annual average PM2.5 concentration is reduced by about 0.5 μg/m3 for both 2016 and 2028. Reductions in state population-weighted average fourth highest MDA8 O3 are 6.6 ppb and 4.3 ppb for 2016 and 2028. The results indicate that there are air quality benefits, and thus health benefits, to be gained from the transition to EVs and that these benefits do not diminish with increasing rates of EV adoption.

Co-Benefits of Energy Structure Transformation and Pollution Control for Air Quality and Public Health until 2050 in Guangdong, China.

In order to mitigate global warming and improve air quality, the transformation of regional energy structures is the most important development pathway. China, as a major global consumer of fossil fuels, will face great pressure in this regard. Aiming toward achieving the global 2 °C warming target in China, this study takes one of the most developed regions of China, Guangdong Province, as the research area in order to explore a future development pathway and potential air quality attainment until 2050, by developing two energy structure scenarios (BAU_Energy and 2Deg_Energy) and three end-of-pipe scenarios (NFC, CLE, and MTFR), and simulating future air quality and related health impacts for the different scenarios using the WRF-Chem model. The results show that under the energy transformation scenario, total energy consumption in Guangdong rises from 296 Mtce (million tons of coal equivalent) in 2015 to 329 Mtce in 2050, with electricity and clean energy accounting for 45% and 35%. In 2050, the transformation of the energy structure leads to 64%, 75%, and 46% reductions in the emissions of CO2, NOx, and SO2 compared with those in 2015. Together with the most stringent end-of-pipe control measures, the emissions of VOCs and primary PM2.5 are effectively reduced by 66% and 78%. The annual average PM2.5 and MDA8 (daily maximum 8 h O3) concentrations in Guangdong are 33.8 and 85.9 μg/m3 in 2015, with 63.4 thousand premature deaths (95% CI: 57.1-70.8) due to environmental exposure. Under the baseline scenario, no improvement is gained in air quality or public health by 2050. In contrast, the PM2.5 and MDA8 concentrations decline to 21.7 and 75.5 μg/m3 under the scenario with energy structure transformation, and total premature deaths are reduced to 35.5 thousand (31.9-39.5). When further combined with the most stringent end-of-pipe control measures, the PM2.5 concentrations decrease to 16.5 μg/m3, but there is no significant improvement for ozone, with premature deaths declining to 20.6 thousand (18.5-23.0). This study demonstrates that the transformation of energy structure toward climate goals could be effective in mitigating air pollution in Guangdong and would bring significant health benefits. Compared with the end-of-pipe control policies, transformation of the energy structure is a more effective way to improve regional air quality in the long term, and synergistic promotion of both is crucial for regional development.

PM2.5 attributable health disparities and distributional benefits of alternative National Ambient Air Quality Standards in the continental United States

Background & Aims: Racial, ethnic, and socioeconomic disparities in exposure to PM2.5 in the United States is well known. However, there is limited understanding of the magnitude of disparities in PM-attributable health effects and distributional benefits of policies, such as potential alternative National Ambient Air Quality Standards (NAAQS) currently being considered by EPA. Methods: We use fine scale air pollution, mortality and morbidity data, as well as new race- ethnicity stratified concentration response functions as inputs for a health impact assessment to determine the distribution of PM2.5 health burdens across race/ethnicity and poverty categories throughout the United States in 2015. Using a rollback approach, we examine the potential distribution of benefits of a 10 µg/m3 and 8 µg/m3 annual standard. We also explore the sensitivity of the model outputs to the different inputs. Results: In 2015, Black Americans experienced three times as many PM2.5-attributable related deaths per capita compared to all other races. The burden of PM-attributable asthma ED visits for non-white Americans were up to six times higher compared to white Americans. We find that an 8 µg/m3 alternative standard could avoid nearly 19,000 premature deaths and 11,500 respiratory emergency room visits. Mortality benefits for the 8 µg/m3 standard are three to four times higher than those for the 10 µg/m3 alternative standard. Per-capita mortality risk reductions for Black Americans are three times the rate of all other races and individuals with incomes ≤ 2X poverty line experience 30% higher mortality benefits under more protective PM NAAQS. Conclusions: These findings indicate that more protective NAAQS would provide some reduction in the magnitude of racial/ethnic and socioeconomic disparities in health burdens of air pollution exposure. Despite this, significant residual disparities would remain, suggesting that supplemental policy approaches would be needed to further alleviate the disparity in PM-attributable health burdens.

Influence of phase change materials on thermal comfort, greenhouse gas emissions, and potential indoor air quality issues across different climatic regions: A critical review

Influence of phase change materials on thermal comfort, greenhouse gas emissions, and potential indoor air quality issues across different climatic regions: A critical review

Evaluating Actions to Improve Air Quality at University Hospitals Birmingham NHS Foundation Trust

Air pollution is the single largest environmental risk to human health in the UK, exerting a major healthcare sector burden and exacerbating health and social inequalities. The NHS Long Term Plan commits the healthcare sector to reducing emissions from all sources, however, to date few Acute NHS Trusts have implemented air quality focused sustainability plans. In this case study, we assess potential air quality improvement actions at University Hospitals Birmingham NHS Foundation Trust’s, Queen Elizabeth Hospital in Birmingham, UK as a test case for NHS sustainability actions. We generate an evidenced based, prioritized shortlist of actions to mitigate emissions and protect patients, staff, and local communities from air pollution exposure. The project supports adoption of an evidence-based, contextually relevant, approach to air quality management within healthcare provision. The methodology used could be employed by organizations with similar goals to address environmental concerns.

US Clean Energy Futures—Air Quality Benefits of Zero Carbon Energy Policies

In this work, we compare the air quality benefits of a variety of future policy scenarios geared towards controlling EGU (electricity generating units) emissions between the present-day conditions and 2050. While these policies are motivated by reducing CO2 emissions, they also yield significant co-benefits for criteria pollutants, such as ozone and PM2.5. An integrated set of clean energy policies were examined to assess the time-varying costs and benefits of a range of decarbonization strategies, including business as usual and the Affordable Clean Energy plan, with a primary focus on others that look to achieve very low, if not zero, CO2 emissions from the EGU sector by 2050. Benefits assessed include mitigation of greenhouse gas emissions as well as air quality co-benefits. In this introductory work, we describe the potential air quality changes from various clean air policies, to set the stage for upcoming work looking at health and monetized benefits. Emission changes for key pollutants are forecast using the Integrated Planning Model (IPM), which are then transformed into emission inputs for the Community Multiscale Air Quality Model (CMAQ). For all primary scenarios considered that achieve large greenhouse gas decreases, significant reductions in ozone and PM are realized, mainly in the eastern US, and all policies produce air quality benefits.

Pollutant Emissions Reporting and Performance Considerations for Hydrogen–Hydrocarbon Fuels in Gas Turbines

Abstract Hydrogen (H2) fuel for gas turbines is a promising approach for long duration storage and dispatchable utilization of intermittent renewable power. A major global discussion point, however, is the potential air quality impact of hydrogen combustion associated with nitrogen oxide (NOX) emissions. Indeed, several studies in the combustion literature have reported elevated NOX concentrations in terms of dry ppmv NOX at 15% oxygen (O2) as a fuel's H2 fraction is increased. Yet, as emphasized in this work, this practice of directly comparing emissions based on dry ppmv at a reference O2 concentration (ppmvdr) is inappropriate across hydrogen and hydrocarbon fuel blends due to differing concentration changes induced by drying and referencing the corresponding exhaust gasses. This paper addresses three distinct approaches for comparing emissions consistently across fuel blends. Furthermore, it presents examples that quantify the differences in the apparent pollutant emissions between each approach and the usual ppmvdr reporting practice across the full range of hydrogen–methane mixture ratios. In all of the considered approaches, ppmvdr emissions values are shown to be inflated for H2 fuel blends relative to hydrocarbon fuels, making them unsuitable for direct comparisons of emissions among conventional and alternative fuels.

The prospected air quality measurements for further unconventional natural gas developments in China based on the United States experience

The technological innovation of horizontal drilling and high-volume hydraulic fracturing has promoted the development of unconventional natural gas (UNG) production worldwide, and hence has aroused public concern about the air pollution it may bring about. In this study, we have provided (1) an overview of the study on air pollutants from UNG emissions in the USA, focusing on both the air pollutant characterization and their related observation technologies/platforms; and (2) the potential air quality measurements of UNG development emerging in China. This study will provide useful information for Chinese environmental researchers and the local governments to deal with related air quality issues.

Machine learning techniques to improve the field performance of low-cost air quality sensors

Abstract. Low-cost air quality sensors offer significant potential for enhancing urban air quality networks by providing higher-spatiotemporal-resolution data needed, for example, for evaluation of air quality interventions. However, these sensors present methodological and deployment challenges which have historically limited operational ability. These include variability in performance characteristics and sensitivity to environmental conditions. In this work, we investigate field “baselining” and interference correction using random forest regression methods for low-cost sensing of NO2, PM10 (particulate matter) and PM2.5. Model performance is explored using data obtained over a 7-month period by real-world field sensor deployment alongside reference method instrumentation. Workflows and processes developed are shown to be effective in normalising variable sensor baseline offsets and reducing uncertainty in sensor response arising from environmental interferences. We demonstrate improvements of between 37 % and 94 % in the mean absolute error term of fully corrected sensor datasets; this is equivalent to performance within ±2.6 ppb of the reference method for NO2, ±4.4 µg m−3 for PM10 and ±2.7 µg m−3 for PM2.5. Expanded-uncertainty estimates for PM10 and PM2.5 correction models are shown to meet performance criteria recommended by European air quality legislation, whilst that of the NO2 correction model was found to be narrowly (∼5 %) outside of its acceptance envelope. Expanded-uncertainty estimates for corrected sensor datasets not used in model training were 29 %, 21 % and 27 % for NO2, PM10 and PM2.5 respectively.

Modeling Reactive Ammonia Uptake by Secondary Organic Aerosol in a Changing Climate: A WRF-CMAQ Evaluation

In addition to the well-constrained inorganic acid-base chemistry of ammonia resulting in fine particulate matter (PM2.5) formation, ammonia also reacts with certain organic compounds in secondary organic aerosol (SOA) to produce less basic nitrogen-containing organic compounds. In this study, the potential meteorology and air quality impacts of the heterogeneous uptake of NH3 by SOA are investigated using the WRF-CMAQ two-way coupled model, which calculates the two-way radiative forcing feedback caused by aerosol between meteorology and chemistry in a single simulation. Simulations with and without the NH3-SOA uptake are performed over the contiguous US for July 2014 and July 2050 under the RCP 8.5 IPCC scenario to study the potential impact of climate change. A comparison with multiple observation network data shows that the NH3-SOA uptake improves the model performance for PM2.5 prediction (bias reduced from −22% to −17%), especially the underestimation of organic carbon over the Southeastern US (bias reduced from −17% to −7%). Secondly, the addition of the NH3-SOA chemistry significantly impacts the concentration of NH3 and NH4+, thus affecting the modeled particle acidity. Including the NH3-SOA uptake also impacts the meteorological conditions through the WRF-CMAQ two-way feedback. Moreover, the impact on meteorological conditions results in different windspeed or dispersion conditions, thus affecting air quality predictions. Finally, simulations including the NH3-SOA uptake under the warmer climate conditions of 2050 show a smaller impact on air quality predictions than it does under current climate conditions. This study confirms the importance and necessity of including NH3-SOA chemistry in air quality predictions.