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Accurate meteorological fields are imperative for correct air quality modeling through their influence on the various chemical species in the atmosphere. In this study, the simulations from the Community Multiscale Air Quality (CMAQ) model were conducted with meteorological fields generated by the Weather Research and Forecasting (WRF) using the original and observation nudging approaches to investigate if the better model performances for PM 2.5 , O 3 , and their related precursors in China were produced from the latter. Two pollution episodes (one for PM 2.5 and another for O 3 ) in 2018 were selected on the basis of the observations at the monitoring supersites in Xianghe and Taizhou cities. The results showed that the Nudging cases had better model performances on all meteorological parameters with higher values of index of agreement (IOA) and lower values of mean bias (MB). It was found that the Nudging case improved model performances for PM 2.5 and its chemical components at the Xianghe site with lower values of normalized mean bias (NMB) and higher values of correlation coefficient (R) than the base case. The results for the regional PM 2.5 over China indicated that the Nudging case reproduced the spatial patterns of mean PM 2.5 concentrations in the 367 cities with the NMB value of –31%, much better than –42.0% in the base case. During the O 3 pollution episode, the Nudging case improved the model performances for O 3 , CO, NO 2 , and VOCs with lower NMB values and higher R values than the base case. The results of regional O 3 over China revealed that the Nudging case reproduced the spatial patterns of observed mean O 3 concentrations in the 367 cities very well with the NMB value of 0.97%, much lower than –5.67% in the base case. The results of this study have great implications for better simulations of meteorology and air quality.

Utilizing a recent observational dataset of particulate matter with diameters less than 2.5 µm (PM2.5) in North China, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in the adjacent two months of December 2015 and January 2016, accompanied by distinct meteorological modulations. The seesaw pattern is postulated to be linked to a super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian winter monsoon (EAWM) and the associated low-level southerly wind anomaly reduced planetary boundary layer (PBL) height, favoring strong haze formation. This circulation pattern was completely reversed in the following month, in part due to a sudden phase change of the AO from positive to negative and the beginning of a decay of the El Niño, which enhanced the southward shift of the upper tropospheric jet from December to January relative to climatology, leading to an enhanced EAWM and substantially lower haze formation. This sub-seasonal change in circulation is also robustly found in 1982–1983 and 1997–1998, implicative of a general physical mechanism dynamically linked to El Niño and the AO. Numerical experiments using the Weather Research and Forecasting (WRF) Community Multiscale Air Quality (CMAQ) model were used to test the modulation of the meteorological conditions on haze formation. With the same emission, simulations for three super El Niño periods (1983, 1997 and 2015) robustly show higher PM2.5  concentrations under the mature phase of the super El Niño, but substantially lower PM2.5 concentrations during the decay phase of El Niño (and the sudden AO phase change), further verifying the modulation effect of the sub-seasonal circulation anomaly on PM2.5 concentrations in North China.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate.

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM 2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding “2+26” cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the “2+26” cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM 2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the “2+26” cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

Air quality in China has been undergoing significant changes due to the implementation of extensive emission control measures since 2013. Many observational and modeling studies investigated the formation mechanisms of fine particulate matter (PM 2.5 ) and ozone (O 3 ) pollution in the major regions of China. To improve understanding of the driving forces for the changes in PM 2.5 and O 3 in China, a nationwide air quality modeling study was conducted from 2013 to 2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. In this study, the model predictions were evaluated using the observation data for the key pollutants including O 3 , sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and PM 2.5 and its major components. The evaluation mainly focused on five major regions, that is , the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Chengyu Basin (CY), and the Fenwei Plain (FW). The CMAQ model successfully reproduced the air pollutants in all the regions with model performance indices meeting the suggested benchmarks. However, over-prediction of PM 2.5 was noted in CY. NO 2 , O 3, and PM 2.5 were well simulated in the north compared to the south. Nitrate (NO 3 − ) and ammonium (NH 4 + ) were the most important PM 2.5 components in heavily polluted regions. For the performance on different pollution levels, the model generally over-predicted the clean days but underpredicted the polluted days. O 3 was found increasing each year, while other pollutants gradually reduced during 2013–2019 across the five regions. In all of the regions except PRD (all seasons) and YRD (spring and summer), the correlations between PM 2.5 and O 3 were negative during all four seasons. Low-to-medium correlations were noted between the simulated PM 2.5 and NO 2 , while strong and positive correlations were established between PM 2.5 and SO 2 during all four seasons across the five regions. This study validates the ability of the CMAQ model in simulating air pollution in China over a long period and provides insights for designing effective emission control strategies across China.

This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/. Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN, and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (−0.1%) and 0.4% (−5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO42- by −23.0% (−27.7%), −12.5% (−18.9%) and −7.9% (−14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM2.5 due to the overestimations of SO42-, NH4+, NO3-, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. This work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.

We present a regional‐scale integrated modeling system (IMS) that includes Environmental Policy Integrated Climate (EPIC), Weather Research and Forecast (WRF), Community Multiscale Air Quality (CMAQ), and Soil and Water Assessment Tool (SWAT) models. The centerpiece of the IMS is the Fertilizer Emission Scenario Tool for CMAQ (FEST‐C), which includes a Java‐based interface and EPIC adapted to regional applications along with built‐in database and tools. The SWAT integration capability is a key enhanced feature in the current release of FEST‐C v1.4. For integrated modeling demonstration and evaluation, FEST‐C EPIC is simulated over three individual years with WRF/CMAQ weather and N deposition. Simulated yearly changes in water and N budgets along with yields for two major crops (corn grain and soybean) match those inferred from intuitive physical reasoning and survey data given different‐year weather conditions. Yearlong air quality simulations with an improved bidirectional ammonia flux modeling approach directly using EPIC‐simulated soil properties including NH3 content helps reduce biases of simulated gas‐phase NH3 and NH4+ wet deposition over the growing season. Integrated hydrology and water quality simulations applied to the Mississippi River Basin show that estimated monthly streamflow and dissolved N near the outlet to the Gulf of Mexico display similar seasonal patterns as observed. Limitations and issues in different parts of the integrated multimedia simulations are identified and discussed to target areas for future improvements. Plain Language Summary Computer modeling tools with land‐water‐air processes are important for understanding nutrient cycling and its negative impacts on air and water quality. We have developed an integrated modeling system that includes agriculture, atmosphere, and hydrology components. The centerpiece of the system is a computer system that includes an agricultural ecosystem model and tools used to connect different modeling components. The agricultural system can conduct simulations for 42 types of grassland and cropland with the influence of site, soil, and management information along with weather and nitrogen deposition from the atmosphere component. An air quality computer model then uses information from the agricultural model, such as how much ammonia is in the soil, to predict how much ammonia gets in the air. Then, the watershed hydrology and water quality model uses the information from the agricultural and atmospheric models to understand the influence of agriculture and atmosphere on water quality. The paper demonstrates and evaluates the integrated modeling system on issues mainly related to N cycling. The system performs reasonably well in comparison with survey and observation data given the configured modeling constraints. The paper also identifies and discusses the advantages and limitations in each part of the system for future applications and improvements. Key Points Modeling components representing agriculture, atmosphere, and hydrology are integrated and evaluated Integrated agriculture, hydrology, and water quality respond to different‐year weather conditions as expected Air quality linked with simulated agriculture improves NH3 flux estimation and results in better performance of N cycling in atmosphere

Currently, significant efforts are being made to enhance the performance of the National Institute of Environmental Research (NIER) operational model. However, the model performance concerning Aerosol Optical Depth (AOD) estimation remains uninvestigated. In this study, three different estimation methods for AOD were implemented using the NIER operational model and validated with satellite and ground observations. In the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) method, AOD exponentially increases with relative humidity owing to a hygroscopic growth factor. However, alternative methods show better performance, since AOD estimation considers the size dependency of aerosol particles and is not sensitive to high relative humidity, which reduces the high AOD in areas with large cloud fractions. Although some R values are significantly low, especially for a single observational comparison and small numerical domain analysis, one of the alternative estimation methods achieves the best performance for diagnosing AOD in the East Asia region.

In the study, crop residue burning (CRB) emissions were estimated based on field surveys and combustion experiments to assess the impact of the CRB on particulate matter over South Korea. The estimates of CRB emissions over South Korea are 9514, 8089, 4002, 2010, 172,407, 7675, 33, and 5053 Mg year−1 for PM10, PM2.5, OC, EC, CO, NOx, SO2, and NH3, respectively. Compared with another study, our estimates in the magnitudes of CRB emissions were not significantly different. When the CRB emissions are additionally considered in the simulation, the monthly mean differences in PM2.5 (i.e., △PM2.5) were marginal between 0.07 and 0.55 μg m−3 over South Korea. Those corresponded to 0.6–4.3% in relative differences. Additionally, the △PM10 was 0.07–0.60 μg m−3 over South Korea. In the spatial and temporal aspects, the increases in PM10 and PM2.5 were high in Gyeongbuk (GB) and Gyeongnam (GN) provinces in June, October, November, and December.

Ozone pollution is formed through complex chemical and physical processes closely associated with emissions, photochemical reactions, and meteorological conditions. The objective of this study is to quantify the contributions of meteorological chemical formation, vertical transport, and horizontal transport to air quality during spring and summer in different regions of the Sichuan Basin. The Community Multi-scale Air Quality (CMAQ) with the Integrated Process Rate (IPR) was employed to simulate the months of April and July 2021 in the Sichuan Basin. The results indicate that both the spring and summer chemical formation of ozone in the urban centre show negative values, while the surrounding urban areas contribute positively, with chemical formation ranging from 0 to 10 μg·m −3 . The maximum ozone level due to horizontal transport in the urban centre exceeds 20 μg·m −3 , whereas horizontal transport in the surrounding urban areas exhibits negative values, with transport contributions concentrated within the range of −5 to 0 μg·m −3 . The vertical transport in the central and southern parts of the basin shows positive values, with transport contributions ranging from 0 to 10 μg·m −3 , and the urban centre exhibits relatively stronger vertical transport with contributions ranging from 10 to 20 μg·m −3 . Although the chemical formation contribution in the urban centre is relatively small due to high nitrogen oxide emissions, vertical and horizontal transport play significant roles and are among the key factors contributing to ozone pollution formation.