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Around 1997, when JCAP (the Japan Clean Air Program) began, Japan’s atmospheric environment did not meet the environmental standards for NO2 and suspended particle matters (SPM), and strict reduction requirements for automobile exhaust gas were required. To achieve environmental standards, further cooperation between the automobile technology and fuel technology sectors was needed. In Europe and the United States, Auto-Oil programs were being implemented to reduce automobile exhaust gas, and JCAP was established as an Auto-Oil program in Japan. The Air Quality Model Study was one of the research themes and research activities continued for a total of 21 years, including JCAP I/II and JATOP I/II/III (the Japan AuTo Oil Program). JATOP was the successor program of JCAP. This paper describes the outline and main results of the JCAP/JATOP Air Quality Model Study.

Dry deposition is an important loss process for atmospheric particles and can be a significant part of total deposition estimates calculated for critical loads analyses. However, algorithms used in large-scale air quality and atmospheric chemistry models to predict particle deposition velocity as a function of particle size are highly uncertain. Many of these algorithms, although derived from a common heritage, predict vastly different particle deposition velocities for a given particle diameter even under identical environmental conditions for major land use classes. Even more problematic, for vegetated landscapes (forests, in particular) the algorithms do not agree very well with available measurements. In this work, we perform a sensitivity study to estimate how significant the uncertainties in particle deposition algorithms may be in an air quality model's predictions of ground-level fine particle concentrations, particle deposition and overall total deposition of nitrogen and sulfur. Our results suggest that fine particle concentration predictions at the surface may vary by 5-15% depending on the choice of particle deposition velocity algorithm, while particle dry deposition is affected to a much greater extent with differences among algorithms >200%. Moreover, if accumulation mode particle dry deposition measurements over forests are correct, then dry particle deposition and total elemental deposition to these landscapes may be much larger than is typically simulated by current air quality and atmospheric chemistry models, calling into question commonly available estimates of total deposition and their use in critical loads analyses. Since accurate predictions of atmospheric particle concentrations and deposition are critically important for future air quality, weather and climate models and management of pollutant deposition to sensitive ecosystems, an investment in new dry deposition measurements in conjunction with integrated modelling efforts seems not only justified but vitally necessary to advance and improve the treatment of particle dry deposition processes in atmospheric models.

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty.

High-resolution air quality models (AQMs) are critical for real-time air quality forecasting and exposure assessment, although their computational costs increase cubically with resolution. Quantifying model sensitivity to resolution is therefore crucial for developing effective forecasting systems. This study conducts a systematic model intercomparison of three widely used AQMs (CAMx, CMAQ, NAQPMS) under identical input conditions at 45, 15, and 5 km resolutions to forecast PM2.5 and O3 in the North China Plain during 2021. Results indicate distinct, model-dependent responses to grid refinement. NAQPMS achieves the optimal PM2.5 forecasting performance at 5 km, with improvements in nearly all evaluated statistics. CMAQ excels in O3 prediction at 5 km resolution, with RMSE reducing 6.48 μg/m3 relative to the coarsest grids. We also found that terrain complexity significantly influences these resolution-dependent biases, leading to a substantial 19.51% reduction in NMB in the CAMx PM2.5 simulation over mountain areas. Moreover, the evaluation of 10-day forecasting accuracy suggests that a high-resolution setting is recommended for NAQPMS and CMAQ, whereas a coarser resolution is sufficient for CAMx. These findings underscore that optimizing real-time forecasting strategies requires a critical investigation of inter-model physicochemical discrepancies rather than universally pursuing higher resolution.

Dry deposition of aerosols from the atmosphere is an important but poorly understood and inadequately modeled process in atmospheric systems for climate and air quality. Comparisons of currently used aerosol dry deposition models to a compendia of published field measurement studies in various landscapes show very poor agreement over a wide range of particle sizes. In this study, we develop and test a new aerosol dry deposition model that is a modification of the current model in the Community Multiscale Air Quality (CMAQ) model. The new model agrees much better with measured dry deposition velocities across particle sizes. The key innovation is the addition of a second inertial impaction term for microscale obstacles such as leaf hairs, microscale ridges, and needleleaf edge effects. The most significant effect of the new model is to increase the mass dry deposition of the accumulation mode aerosols in CMAQ. Accumulation mode mass dry deposition velocities increase by almost an order of magnitude in forested areas with lesser increases for shorter vegetation. Peak PM2.5 concentrations are reduced in some forested areas by up to 40% in CMAQ simulations. Over the continuous United States, the new model reduced PM2.5 by an average of 16% for July 2018 at the Air Quality System monitoring sites. For summer 2018 simulations, bias and error of PM2.5 concentrations are significantly reduced, especially in forested areas. Plain Language Summary Aerosol dry deposition is an important sink for atmospheric particles that are a health hazard and a significant climate forcer. Uncertainties in modeling aerosol dry deposition hamper accurate predictions of air quality and climate. A new aerosol dry deposition model is developed that better agrees with observations of aerosol dry deposition velocity for a variety of vegetation such as forests, grasslands, and water surfaces. This improved aerosol dry deposition model when incorporated into air quality and climate models will improve the accuracy of model predictions. Key Points New aerosol deposition velocity model agrees better with observations than current models Impaction on microscale obstacles such as leaf hairs is key process New aerosol deposition velocity model increases dry deposition of PM2.5 compared to the current Community Multiscale Air Quality model

At the end of the dry season, March and April in Southeast Asia (SEA), agricultural refuse burnings occur over the region, mainly in the countries of Myanmar, Thailand, Laos, Cambodia and Vietnam, in preparation for the wet rice plantation. In this study, the impact of biomass burnings at the height of the burning period in March 2019 in mainland SEA on air quality and pollutant transport is modelled using the Weather Research Forecast WRF-Chem air quality model with emission input from the National Center for Atmospheric Research (NCAR) Fire Emission Inventory from NCAR (FINN). FINN is derived from satellite remote sensing data and species emission factors. A simulation of the dispersion of pollutants from biomass burnings from 13 to 19 March 2019, when the burnings was most intense, was performed. Validation of the model prediction using observed meteorological and pollutant data such as AOD measurements on ground from AERONET (Aerosol Robotic Network) and data from MODIS and CALIPSO satellites is carried out at various sites in the region. The results show that impact on air quality was most pronounced in Thailand and Laos but the effect of biomass burnings in mainland SEA at the end of the dry season is widespread in terms of pollutant dispersion and population exposure over the whole region and beyond. It is also shown that the transport of pollutants from biomass burnings in SEA to southern China, Taiwan and beyond is facilitated by the Truong Son mountain range, when under westerly wind, acting as a launching pad to uplift the pollutant plumes to higher altitude which then can be dispersed widely and transported farther from the biomass burning sources in Thailand and Laos.

We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada’s four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO2, PM2.5, and O3 during a “pre-lockdown” period (15 February–14 March 2020) and a “lockdown” period (22 March–2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010–2019). Higher-than-usual seasonal declines in mean daily NO2 were observed for the pre-lockdown to lockdown periods in 2020. For PM2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O3 all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO2 surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM2.5. O3 surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but Ox (odd oxygen), like NO2 and PM2.5, decreased as expected over these cities.

Accurate prediction of ambient PM2.5 concentrations using air quality models can provide governments with information for public health alerts. However, due to large uncertainties of input parameters and over‐simplification of the chemical mechanism, the model simulations tend to have a certain deviation from the observations. To provide an insight into the discrepancy and to explain the contributors to the model bias, we propose here a machine learning based method to identify the contributors to PM2.5 simulation biases. A fully connected deep neural network (noted as FCNN) was designed to correct the PM2.5 biases between the simulations from a common air quality model (i.e., Community Multiscale Air Quality, CMAQ) and observations with meteorological and pollutants variables. The FCNN was applied in two polluted regions in China including Beijing‐Tianjin‐Hebei (BTH) and Yangtze River Delta (YRD) in 2015, exhibiting excellent performance in reducing the root mean square error of annual PM2.5 by 46.6% and 37.2%, respectively. The relative contribution of each input feature for the bias correction was also estimated from the FCNN. Results suggest that the temperature and humidity exhibit the greatest contribution to the PM2.5 simulation bias among all meteorological factors, probably due to their high association with the physical and chemical reaction conditions. NO2 and SO2 concentrations and associated biases were also found to be crucial to CMAQ model accuracy, implying the importance of NO2‐ and SO2‐related reaction for PM2.5 formation. The study also revealed a cumulative effect of pollution and an enhancement effect of atmospheric oxidation on the formation of heavy pollution. Plain Language Summary The study aims to provide a better understanding of the causes of the Community Multiscale Air Quality model (CMAQ) bias during the bias correction, compared to exclusively eliminating the inherent bias using statistical models. We proposed fully connected deep neural networks in two polluted regions in China ‐ Beijing‐Tianjin‐Hebei and Yangtze River Delta ‐ in 2015 as bias correction, during which the relative contribution of the variables under different meteorological conditions across different seasons has been calculated. We found that the temperature and humidity exhibit the greatest contribution to the PM2.5 simulation bias among all meteorological factors, probably due to their high association with the physical and chemical reaction conditions. NO2 and SO2 concentrations and biases were also found to be crucial to the improvement of CMAQ model accuracy, implying the importance of NO2‐ and SO2‐related reaction for PM2.5 formation. By introducing cutting edge artificial intelligence (AI) technologies, we provided an insight into the discrepancy and explain the contributors to the model bias in severe polluted regions, which can help with the improvement in PM2.5 forecasts and timely control strategy design. Key Points The under‐prediction of PM2.5 by the CMAQ model over two polluted regions in China prevails throughout the year and is particularly significant in heavy pollution The fully connected neural network effectively captured the pattern of the PM2.5 simulation bias NO2 & SO2 concentrations and biases are found to be the keys to improving the accuracy of the CMAQ model

Prescribed burning (PB) is practiced throughout the USA, most extensively in the southeast, for the purpose of maintaining and improving the ecosystem and reducing wildfire risk. However, PB emissions contribute significantly to trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, PB can lead to major health and environmental problems. We developed a PB impact forecasting system to facilitate the dynamic management of air quality by modulating PB activity. In our system, a new decision tree model predicts burn activity based on the weather forecast and historic burning patterns. Emission estimates for the forecast burn activity are input into an air quality model, and simulations are performed to forecast the air quality impacts of the burns on trace gas and particulate matter concentrations. An evaluation of the forecasts for two consecutive burn seasons (2015 and 2016) showed that the modeling system has promising forecasting skills that can be further improved with refinements in burn area and plume rise estimates. Since 2017, air quality and burn impact forecasts are being produced daily with the ultimate goal of incorporating them into the management of PB operations.

Historical haze episodes (2013–16) in Guangzhou were examined and classified according to synoptic weather systems. Four types of weather systems were found to be unfavorable, among which “foreside of a cold front” (FC) and “sea high pressure” (SP) were the most frequent (>75% of the total). Targeted case studies were conducted based on an FC-affected event and an SP-affected event with the aim of understanding the characteristics of the contributions of source regions to fine particulate matter (PM 2.5 ) in Guangzhou. Four kinds of contributions—namely, emissions outside Guangdong Province (super-region), emissions from the Pearl River Delta region (PRD region), emissions from Guangzhou–Foshan–Shenzhen (GFS region), and emissions from Guangzhou (local)—were investigated using the Weather Research and Forecasting–Community Multiscale Air Quality model. The results showed that the source region contribution differed with different weather systems. SP was a stagnant weather condition, and the source region contribution ratio showed that the local region was a major contributor (37%), while the PRD region, GFS region and the super-region only contributed 8%, 2.8% and 7%, respectively, to PM 2.5 concentrations. By contrast, FC favored regional transport. The super-region became noticeable, contributing 34.8%, while the local region decreased to 12%. A simple method was proposed to quantify the relative impact of meteorology and emissions. Meteorology had a 35% impact, compared with an impact of -18% for emissions, when comparing the FC-affected event with that of the SP. The results from this study can provide guidance to policymakers for the implementation of effective control strategies.