2024
ACEIC: a comprehensive anthropogenic chlorine emission inventory for China
Siting Li, Yiming Liu#, Yuqi Zhu, Yinbao Jin, Yingying Hong, Ao Shen, Yifei Xu, Haofan Wang, Haichao Wang, Xiao Lu, Shaojia Fan, Qi Fan# (# corresponding author)
Atmospheric Chemistry and Physics 2024
Chlorine species play a crucial role as precursors to Cl radicals, which can significantly impact the atmospheric oxidation capacity and influence the levels of trace gases related to climate and air quality. Several studies have established a chlorine emission inventory in China in recent years, but the emission remains uncertain and requires further investigation. The Anthropogenic Chlorine Emission Inventory for China (ACEIC) was the first chlorine emission inventory for China based on local data developed in our previous study, which only includes the emissions from coal combustion and waste incineration. In this study, we updated this inventory to include data for a more recent year (2019) and expanded the range of species considered (HCl, fine particulate Cl−, Cl2, and hypochlorous acid (HOCl)) and the number of anthropogenic sources (41 specific sources). Compared with previous studies, this updated inventory considered more anthropogenic sources, used more localized emission factors, and adopted more refined estimation methods. The total emissions of HCl, fine particulate Cl−, Cl2, and HOCl in mainland China for the year 2019 were estimated to be 361 (−18 % to 27 %), 174 (−27 % to 59 %), 18 (−10 % to 15 %), and 79 (−12 % to 18 %) Gg, respectively. To facilitate analysis, we aggregated the chlorine emissions from various sources into five economic sectors: power, industry, residential, agriculture, and biomass burning. HCl emissions were primarily derived from the industry (43 %), biomass burning (38 %), and residential (13 %) sectors. The biomass burning and industry sectors accounted for 74 % and 19 % of the fine particulate Cl− emissions, respectively. Residential and industry sectors contributed 61 % and 29 % of the total Cl2 emissions. HOCl emissions were predominantly from the residential sector, constituting 90 % of the total emissions. Notably, the usage of chlorine-containing disinfectants was identified as the most significant source of Cl2 and HOCl emissions in the residential sector. Geographically, regions with high HCl and fine particulate Cl− emissions were found in the North China Plain, northeastern China, central China, and the Yangtze River Delta, whereas the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei regions exhibited elevated levels of Cl2 and HOCl emissions. Regarding monthly variation, emissions of HCl and fine particulate Cl− were higher during early spring (February to April) and winter (December to January) due to intensified agricultural activities, while Cl2 and HOCl emissions were higher in the summer months due to increased demand for water disinfection. We incorporated this emission inventory into the chemical transport model and found the simulated concentrations of chlorine species agreed reasonably well with the observations, which suggested the relatively faithful estimations of their emissions. This updated inventory contributes to a better understanding of anthropogenic sources of chlorine species and can aid in the formulation of emission control strategies to mitigate secondary pollution in China.
Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002–2020
Yaohan Xian, Yang Zhang#, Zhihong Liu, Haofan Wang, Tianxin Xiong (# corresponding author)
Science of The Total Environment (STE) 2024
In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality–Integrated Source Apportionment Method (CMAQ–ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21–40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> −20 μg/m3) after 2011 due to pollution control measures.
MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model
Haofan Wang, Jiaxin Qiu, Yiming Liu#, Qi Fan, Xiao Lu, Yang Zhang, Kai Wu, Ao Shen, Yifei Xu, Yinbao Jin, Yuqi Zhu, Jiayin Sun, Haolin Wang (# corresponding author)
Atmospheric Environment (AE) 2024 Spotlight
The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) refines emission inventories by providing detailed spatial (horizontal and vertical), temporal, and species allocations, enhancing the accuracy of CMAQ performance. Its efficient algorithm and modular design offer flexibility for managing both gridded and tabulated inventories, widely used in various sectors. In addition, the shapefiles with specific shapes supported by MEIAT-CMAQ can address the allocation challenges in transportation emissions. The evaluation of MEIAT-CMAQ, using model-ready inventories before (BASE scenario), and after allocation without (EXPR scenario) or with (EXPR-V scenario) vertical allocation, demonstrates significant improvements in the mean bias (MB) of gaseous pollutants (O3, NO2, CO). In both the EXPR and EXPR-V scenarios, the MB for O3 exhibits notable enhancements, with respective improvements of 5.7% and 26.9%. For NO2, corresponding MB improvements are even more pronounced, reaching 27.6% and 61.7% in the EXPR and EXPR-V scenarios, respectively. Likewise, enhancements are observed in the MBs of CO, demonstrating increases of 8.4% and 45.2% in the EXPR and EXPR-V scenarios, respectively. Moreover, with regard to spatial accuracy, the incorporation of the MEIAT-CMAQ model yields significant improvements. Specifically, in the EXPR scenarios, spatial accuracy for O3 and NO2 demonstrates respective enhancements of 13.5% and 9.5%. Furthermore, the inclusion of vertical allocation leads to additional enhancements in CO, NO2, and PM2.5, resulting in improvements of 17.6%, 16.6%, and 23.2%, respectively. MEIAT-CMAQ provides an efficient method for transforming coarse-resolution emission inventories into high-resolution files directly useable in the model, offering enhanced flexibility for users to select any period for generating model-ready emission files. This capability provides substantial technical support for automating processes within business departments and significantly improves the performance of high-resolution modeling and forecasting.
Source apportionment and formation of warm season ozone pollution in Chengdu based on CMAQ-ISAM
Yaohan Xian, Yang Zhang#, Zhihong Liu, Haofan Wang, Junjie Wang, Chao Tang (# corresponding author)
Urban Climate 2024
In this study, the WRF-CMAQ model integrated with BEM urban canopy model was used to simulate the concentrations of Ozone ( O3 ) and its precursors, NOx, and VOCs, in warm season of Chengdu, conduct source apportionment and formation analysis. The results show that the O3 in Chengdu exhibits a west-high/east-low spatial pattern, attributable to nearly 40% contribution from boundary sources representing the transport role of the Sichuan Basin, regional sources from districts emitting high precursor concentrations, and increasing biogenic contributions from western areas due to rising BVOCs emissions during the warm season. NOx from traffic and VOCs from industrial sources, both prevalent in Chengdu's high urban density areas, chemically react to form O3, making these sectors primary contributors to O3. NOx photochemical reactions producing O3 occur at 150 m–2500 m with peak generation rates of 10 μg/(m3·hr). Ground-level NO titration removal is most significant during heavy traffic (14:00–21:00), ranging from −70 to −200 μg/(m3·hr). O3 is replenished through similar rates of daytime vertical diffusion and nighttime horizontal advection, correlating with urban density across regions. Controlling Chengdu's warm season O3 requires focusing on long-distance external transport and regional precursor emission reductions, with strategies tailored to local urban characteristics.
Measurement report: Assessing the impacts of emission uncertainty on aerosol optical properties and radiative forcing from biomass burning in peninsular Southeast Asia
Yinbao Jin, Yiming Liu#, Xiao Lu, Xiaoyang Chen, Ao Shen, Haofan Wang, Yinping Cui, Yifei Xu, Siting Li, Jian Liu, Ming Zhang, Yingying Ma, Qi Fan# (# corresponding author)
Atmospheric Chemistry and Physics 2024
Despite significant advancements in improving the dataset for biomass burning (BB) emissions over the past few decades, uncertainties persist in BB aerosol emissions, impeding the accurate assessment of simulated aerosol optical properties (AOPs) and direct radiative forcing (DRF) during wildfire events in global and regional models. This study assessed AOPs (including aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), and aerosol extinction coefficients (AECs)) and DRF using eight independent BB emission inventories applied to the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) during the BB period (March 2019) in peninsular Southeast Asia (PSEA), where the eight BB emission inventories were the Global Fire Emissions Database version 4.1s (GFED), Fire INventory from NCAR version 1.5 (FINN1.5), the Fire Inventory from NCAR version 2.5 MOS (MODIS fire detections; FINN2.5 MOS), the Fire Inventory from NCAR version 2.5 MOSVIS (MODIS + VIIRS fire detections; FINN2.5 MOSVIS), Global Fire Assimilation System version 1.2s (GFAS), Fire Energetics and Emissions Research version 1.0 (FEER), Quick Fire Emissions Dataset version 2.5 release 1 (QFED), and Integrated Monitoring and Modelling System for Wildland FIRES project version 2.0 (IS4FIRES), respectively. The results show that in the PSEA region, organic carbon (OC) emissions in the eight BB emission inventories differ by a factor of about 9 (0.295–2.533 Tg M−1), with 1.09 ± 0.83 Tg M−1 and a coefficient of variation (CV) of 76 %. High-concentration OC emissions occurred primarily in savanna and agricultural fires. The OC emissions from the GFED and GFAS are significantly lower than the other inventories. The OC emissions in FINN2.5 MOSVIS are approximately twice as high as those in FINN1.5. Sensitivity analysis of AOD simulated by WRF-Chem to different BB emission datasets indicated that the FINN scenarios (v1.5 and 2.5) significantly overestimate AOD compared to observation (VIIRS), while the other inventories underestimate AOD in the high-AOD (HAOD; AOD > 1) regions range from 15–22.5∘ N, 97–110∘ E. Among the eight schemes, IS4FIRES and FINN1.5 performed better in terms of AOD simulation consistency and bias in the HAOD region when compared to AERONET sites. The AAOD in WRF-Chem during the PSEA wildfire period was assessed, using satellite observations (TROPOMI) and AERONET data, and it was found that the AAOD simulated with different BB schemes did not perform as well as the AOD. The significant overestimation of AAOD by FINN (v1.5 and 2.5), FEER, and IS4FIRES schemes in the HAOD region, with the largest overestimation for FINN2.5 MOSVIS. FINN1.5 schemes performed better in representing AAOD at AERONET sites within the HAOD region. The simulated AOD and AAOD from FINN2.5 MOSVIS always show the best correlation with the observations. AECs simulated by WRF-Chem with all the eight BB schemes trends were consistent with CALIPSO in the vertical direction (0.5 to 4 km), demonstrating the efficacy of the smoke plume rise model used in WRF-Chem to simulate smoke plume heights. However, the FINN (v1.5 and 2.5) schemes overestimated AECs, while the other schemes underestimated it. In the HAOD region, BB aerosols exhibited a daytime shortwave radiative forcing of −32.60 ± 24.50 W m−2 at the surface, positive forcing (1.70 ± 1.40 W m−2) in the atmosphere, and negative forcing (−30.89 ± 23.6 W m−2) at the top of the atmosphere. Based on the analysis, FINN1.5 and IS4FIRES are recommended for accurately assessing the impact of BB on air quality and climate in the PSEA region.
2023
Nighttime ozone in the lower boundary layer: insights from 3-year tower-based measurements in South China and regional air quality modeling
Guowen He, Cheng He, Haofan Wang, Xiao Lu#, Chenglei Pei, Xiaonuan Qiu, Chenxi Liu, Yiming Wang, Nanxi Liu, Jinpu Zhang, Lei Lei, Yiming Liu, Haichao Wang, Tao Deng, Qi Fan, Shaojia Fan (# corresponding author)
Atmospheric Chemistry and Physics (ACP) 2023 Spotlight
Nighttime ozone in the lower boundary layer regulates atmospheric chemistry and surface ozone air quality, but our understanding of its vertical structure and impact is largely limited by the extreme sparsity of direct measurements. Here we present 3-year (2017–2019) measurements of ozone in the lower boundary layer (up to 500 m) from the Canton Tower in Guangzhou, the core megacity in South China, and interpret the measurements with a 1-month high-resolution chemical simulation from the Community Multiscale Air Quality (CMAQ) model. Measurements are available at 10, 118, 168, and 488 m, with the highest (488 m) measurement platform higher than the typical height of the nighttime stable boundary layer that allows direct measurements of ozone in the nighttime residual layer (RL). We find that ozone increases with altitude in the lower boundary layer throughout the day, with a vertical ozone gradient between the 10 and 488 m heights (ΔOH10–488 m) of 3.6–6.4 ppbv hm−1 in nighttime and 4.4–5.8 ppbv hm−1 in daytime. We identify a high ozone residual ratio, defined as the ratio of ozone concentration averaged over nighttime to that in the afternoon (14:00–17:00 LT), of 69 %–90 % in January, April, and October, remarkably higher than that in the other three layers (29 %–51 %). Ozone in the afternoon convective mixing layer provides the source of ozone in the RL, and strong temperature inversion facilitates the ability of RL to store ozone from the daytime convective mixing layer. The tower-based measurement also indicates that the nighttime surface Ox (Ox= O3+NO2) level can be an effective indicator of RL ozone if direct measurement is not available. We further find significant influences of nocturnal RL ozone on both the nighttime and the following day's daytime surface ozone air quality. During the surface nighttime ozone enhancement (NOE) event, we observe a significant decrease in ozone and an increase in NO2 and CO at the 488 m height, in contrast to their changes at the surface, a typical feature of enhanced vertical mixing. The enhanced vertical mixing leads to an NOE event by introducing ozone-rich and NOx-poor air into the RL to enter the nighttime stable boundary layer. The CMAQ model simulations also demonstrate an enhanced positive contribution of vertical diffusion (ΔVDIF) to ozone at the 10 and 118 m heights and a negative contribution at the 168 and 488 m heights during the NOE event. We also observe a strong correlation between nighttime RL ozone and the following day's surface maximum daily 8 h average (MDA8) ozone. This is tied to enhanced vertical mixing with the collapse of nighttime RL and the development of a convective mixing layer, which is supported by the CMAQ diagnosis of the ozone budget, suggesting that the mixing of ozone-rich air from nighttime RL downward to the surface via the entrainment is an important mechanism for aggravating ozone pollution the following day. We find that the bias in CMAQ-simulated surface MDA8 ozone the following day shows a strong correlation coefficient (r= 0.74) with the bias in nighttime ozone in the RL, highlighting the necessity to correct air quality model bias in the nighttime RL ozone for accurate prediction of daytime ozone. Our study thus highlights the value of long-term tower-based measurements for understanding the coupling between nighttime ozone in the RL, surface ozone air quality, and boundary layer dynamics.
Assessment of tropospheric ozone simulations in a regional chemical transport model using GEOS-Chem outputs as chemical boundary conditions
Yuqi Zhu, Yiming Liu#, Siting Li, Haolin Wang, Xiao Lu, Haichao Wang, Chong Shen, Xiaoyang Chen, Pakwai Chan, Ao Shen, Haofan Wang, Yinbao Jin, Yifei Xu, Shaojia Fan, Qi Fan# (# corresponding author)
Science of The Total Environment (STE) 2023
Regional chemical transport models (e.g., Community Multiscale Air Quality (CMAQ) Modeling System) are widely used to simulate the physical and chemical process of regional ozone (O3) pollution and its variation trend in recent years. However, chemical boundary condition (CBC) is an important input of these models and contributes to the model bias against observations. In this study, we develop a tool named GC2CMAQ that provides the CMAQ model with the CBCs from the GEOS-Chem simulation. Two experiments using different CBCs were conducted to evaluate their effect on seasonal O3 simulation in China. The Default experiment utilized the model-default static condition (the relatively clean atmosphere in the eastern United States), and the GC experiment employed the GEOS-Chem simulation results. Compared with the observation, the GC experiment has a much better performance in reproducing elevated O3 levels in the higher troposphere and lower stratosphere during different seasons. Near the earth's surface, the simulated concentrations of pollutants O3 (and PM2.5) in the GC experiment were also closer to the observation in April and July. The accuracy of simulation results in provinces close to the boundary was improved by approximately 20 %–30 % relative to the Default experiment. The CBCs provided by GEOS-Chem enabled a better simulation of stratosphere-troposphere O3 exchange in late spring and early summer, which then affected the pollutant concentration near surfaces through vertical transport. This finding was confirmed by a case study in southwestern Tibet on April 28, 2017, in which we quantified the contributions of different physical and chemical processes to O3 variations at different altitudes using the process analysis method. This study highlights the importance of using a reliable CBC for the regional chemical transport model to derive a better performance of O3 simulation.
Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015–2022 ozone increase in Guangzhou
Nanxi Liu, Guowen He, Haolin Wang, Cheng He, Haofan Wang, Chenxi Liu, Yiming Wang, Haichao Wang, Lei Li, Xiao Lu#, Shaojia Fan# (# corresponding author)
Journal of Environmental Sciences (JES) 2023
Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015–2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015–2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015–2022 ozone increase in Guangzhou.
Modeling regional nitrogen cycle in the atmosphere: Present situation and its response to the future emissions control strategy
Ao Shen, Yiming Liu#, Xiao Lu, Yifei Xu, Yinbao Jin, Haofan Wang, Juan Zhang, Xuemei Wang, Ming Chang, Qi Fan# (# corresponding author)
Science of The Total Environment (STE) 2023
Reactive nitrogen (Nr) cycle in the atmosphere has an important affection on terrestrial ecosystems, which has not been fully understood and its response to the future emissions control strategy is not clear. Taking the Yangtze River Delta (YRD) as an example, we explored the regional Nr cycle (emissions, concentrations, and depositions) and its source apportionment in the atmosphere in January (winter) and July (summer) 2015 and projected its changes under emissions control by 2030 using the CMAQ model. We examined the characteristics of Nr cycle and found that Nr suspends in the air mainly as NO, NO2, and NH3 gases and deposits to the earth's surface mainly as HNO3, NH3, NO3−, and NH4+. Due to the higher NOx than NH3 emissions, oxidized nitrogen (OXN) but not reduced nitrogen (RDN) is the major component in Nr concentration and deposition, especially in January. Nr concentration and deposition show an inverse correlation, with a high concentration in January and low in July but the opposite for deposition. We further apportioned the regional Nr sources for both concentration and deposition using the Integrated Source Apportionment Method (ISAM) incorporated in the CMAQ model. It shows that local emissions are the major contributors and this characteristic is more significant in concentration than deposition, for RDN than OXN species, and in July than in January. The contribution from North China (NC) is important for Nr in YRD, especially in January. In addition, we revealed the response of Nr concentration and deposition to the emission control to achieve the target of carbon peak in the year 2030. After the emission reduction, the relative responses of OXN concentration and deposition are generally about 100 % to the reduction of NOx emissions (~50 %), while the relative responses of RDN concentration are higher than 100 % and the relative responses of RDN deposition are significantly lower than 100 % to the reduction of NH3 emissions (~22 %). Consequently, RDN will become the major component in Nr deposition. The smaller reduction of RDN wet deposition than sulfur wet deposition and OXN wet deposition will raise the pH of precipitation and help alleviate the acid rain problem, especially in July.
Exploring the effects of land use and land cover changes on meteorology and air quality over Sichuan Basin, southwestern China
Yaqiong Lu, Xianyu Yang, Haofan Wang, Mengjiao Jiang, Xiaohang Wen, Xiaoling Zhang, Lixia Meng
Frontiers in Ecology and Evolution (FEE) 2023 Spotlight
Accurate characterization of land use and land cover changes (LULCC) is essential for numerical models to capture LULCC-induced effects on regional meteorology and air quality, while outdated LULC dataset largely limits model capability in reproducing land surface parameters, particularly for complex terrain. In this study, we incorporate land cover data from MODIS in 2019 into the Weather Research and Forecasting (WRF) model to simulate the impacts of LULC on meteorological parameters over the Sichuan Basin (SCB). Further, we conduct Community Multiscale Air Quality (CMAQ) simulations with WRF default LULC and MODIS 2019 to probe the effects on regional air quality. Despite consistency found between meteorological observations and WRF-CMAQ simulations, the default WRF land cover data does not accurately capture rapid urbanization over time compared with MODIS. Modeling results indicate that magnitude changes trigged by LULCC are highly varied across SCB and the impacts of LULCC are more pronounced over extended metropolitan areas due to alteration by urbanization, featured by elevating 2-m temperature up to 2°C and increased planetary boundary layer height (PBLH) up to 400 m. For air quality implications, it is found that LULCC leads to basin-wide O3 enhancements with maximum reaching 21.6 μg/m³ and 57.2 μg/m³ in the daytime and nighttime, respectively, which is mainly attributed to weakening NOx titration effects at night. This work contributes modeling insights into quantitative assessment for impacts of LULCC on regional meteorology and air quality which pinpoints optimization of the meteorology-air quality model.
ISAT v2.0: an integrated tool for nested-domain configurations and model-ready emission inventories for WRF-AQM
Kun Wang, Chao Gao, Kai Wu, Kaiyun Liu, Haofan Wang, Mo Dan, Xiaohui Ji, Qingqing Tong
Geoscientific Model Development (GMD) 2023 Spotlight
The ISAT (Inventory Spatial Allocation Tool) v2.0 is an integrated tool that has been developed to configure nested domains, downscale regional emission inventories, allocate local emission inventories, and generate model-ready emission inventories for the Weather Research and Forecasting (WRF)–Air Quality Numerical Model (AQM). The tool consists of four modules, namely “Prepgrid”, “Downscale”, “Mapinv”, and “Prepmodel”, which are designed to perform specific tasks. The Prepgrid module utilizes a nested-domain configuration algorithm based on WRF-AQM nested rules and the target domain shapefile. The Downscale module establishes a “sub-grid nearest” method to downscale the regional emission inventory based on spatial surrogate, thereby improving the accuracy and computational efficiency of the process. The Mapinv module allocates a user-defined regional- and/or city-level emission inventory to grid level based on the target domain shapefile and the spatial surrogate. Finally, the Prepmodel module generates the model-ready inventories by introducing unique user-friendly emission sector IDs using abbreviations and speciation profiles based on species in the emission inventory and chemical mechanisms, which is available for both the CMAQ and CAMx models. The ISAT v2.0 tool provides a user-friendly solution for model users to configure and run WRF-AQM. And it provides a framework and related algorithms for researchers to develop similar tools for WRF-AQM.
Impacts of land use and land cover changes on local meteorology and PM2.5 concentrations in Changchun, Northeast China
Jiaxin Qiu, Chunsheng Fang, Naixu Tian, Haofan Wang, Ju Wang
Atmospheric Research (AR) 2023 Spotlight
Land use and land cover (LULC) changes have a considerable influence on the surface energy balance, altering regional meteorology and air quality. However, this impact is not quantified in Changchun, an important city in the old industrial base of Northeast China. In this study, based on the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model, the LULC2017 (LULC data in 2017) and LULC2001 (LULC data in 2001) scenarios were simulated for January and July 2017, respectively, to assess the impact of LULC changes on meteorology and fine particulate matter (PM2.5) concentrations in Changchun. The results show that the sensible heat flux in the urban expansion area (UEA) increased during the daytime, reaching a maximum value of 154 W/m² and 162 W/m², respectively, while the latent heat flux decreased during the daytime, reaching a maximum value of 22.84 W/m² and 180.75 W/m², respectively. Consequently, 2 m temperature (T2) increased by 4 °C and 3 °C, respectively; 10 m wind speed (WS10) increased by 1.05 m/s and 1.60 m/s, respectively; and planetary boundary layer height (PBLH) increased by 100 m and 117 m, respectively. These variations in meteorological factors can substantially impact the spatial distribution of air pollutants. In the UEA, PM2.5 concentrations decreased by 34 μg/m³ and 20 μg/m³ in January and July, respectively. The change in SO4²⁻ accounted for approximately 25% of the total concentration change of PM2.5, with a decrease of approximately 5–6 μg/m³ during the nighttime in January. Secondary organic aerosol (SOA) formed from biogenic volatile organic compounds (BVOC) precursors (BSOA) slightly decreased owing to the reduction in croplands dominated by green vegetation. Meanwhile, PM2.5 concentrations in the surrounding areas of the UEA increased significantly in January. The results of the process analysis based on the CMAQ model indicate that the main reason for the spatial variation of PM2.5 concentrations is the enhancement of transport and diffusion in the horizontal and vertical directions in the UEA. In January, the negative contribution of vertical advection (ZADV) and horizontal advection (HADV) processes to PM2.5 in the UEA increased by 25 μg/m³ and 40 μg/m³, respectively. Vertical diffusion (VDIF) process caused an increase in PM2.5 diffusion by 40 μg/m³ and 16 μg/m³ during the daytime and nighttime in the UEA, respectively. In July, the negative contribution of VDIF and HADV processes to PM2.5 increased by 40 μg/m³ and 32 μg/m³ during the nighttime in the UEA, respectively.
2022
Impact of Urbanization on Meteorology and Air Quality in Chengdu, a Basin City of Southwestern China
Haofan Wang, Zhihong Liu, Kai Wu, Jiaxin Qiu, Yang Zhang#, Bangping Ye, Min He (# corresponding author)
Frontiers in Ecology and Evolution (FEE) 2022
Rapid urbanization has the potential to fundamentally perturb energy budget and alter urban air quality. While it is clear that urban meteorological parameters are sensitive to urbanization-induced changes in landscapes, a gap exists in our knowledge about how changes in land use and land cover affect the dynamics of urban air quality. Herein, we simulated a severe O3 episode (10–16 July 2017) and a highly polluted PM2.5 episode (25–30 December 2017) and assessed the changes of meteorological phenomenon and evolution of air pollutants induced by urbanization. We found that the urban expansion area (i.e., land use transition from natural to urban surfaces between 2000 and 2017, UEA) has a significant increase in nocturnal 2-m temperature (T2) with maximum values reaching 3 and 4°C in summer and winter, respectively. In contrast, UEA experienced cooling in the daytime with stronger reductions of T2 in winter than in summer. The T2 variability is primarily attributed to the intense thermal inertia and high heat capacity of the urban canopy and the shadowing effect caused by urbanization. Owing to increased surface roughness and decreased surface albedo as well as shadowing effects, the ventilation index (VI) of UEA increased up to 1,200 m2/s in winter while decreased up to 950 m2/s in summer. Changes in meteorological phenomenon alter physical and chemical processes associated with variations in PM2.5 and O3 concentrations. Urbanization leads to enhanced vertical advection process and weakened aerosol production, subsequently causing PM2.5 levels to decrease by 33.2 μg/m3 during the day and 4.6 μg/m3 at night, respectively. Meanwhile, O3 levels increased by 61.4 μg/m3 at 20:00 due to the reduction of horizontal advection induced by urbanization, while O3 concentrations changed insignificantly at other times. This work provides valuable insights into the effects of urbanization on urban meteorology and air quality over typical megacities, which support informed decision-making for urban heat and air pollution mitigation.
2021
Impact of different urban canopy models on air quality simulation in Chengdu, southwestern China
Haofan Wang, Zhihong Liu, Yang Zhang#, Zhengyang Yu, Chunrong Chen (# corresponding author)
Atmospheric Environment (AE) 2021
Urban air pollution has emerged as a prominent public health concern in megacities and highly developed city clusters. Accurate modeling of urban air quality over complex terrain is challenging due to heterogeneous urban landscapes and multiscale land-atmosphere interactions. In this study, we investigated the applicability of urban canopy models in the Weather Research and Forecast (WRF) model and assessed the impacts of implementing these models on the urban air quality simulation in the Community Multiscale Air Quality (CMAQ) model over the megacity Chengdu, southwestern China. The land use and land cover of Chengdu were updated in WRF by using the land-use products in 2017 from the Moderate-resolution Imaging Spectroradiometer (MODIS). Sensitivity experiments with various urban canopy models were conducted to investigate the feasibility of different urban canopy models on WRF-CMAQ simulations. We found that the SLAB model significantly underestimates NO2 and PM2.5 concentrations, with mean fractional bias in winter (summer) reaching 52.93% (−50.34%) and −102.82% (−23.12%), respectively. Such large biases are mainly attributed to overpredicted wind speeds resulting from the flat structure in the SLAB model. In contrast, the BEM (a multilayer urban canopy model coupled with air-conditioning systems) model yields the best model performance in both winter and summer, with mean fractional errors of 33.15% (38.96%) and 34.10% (33.15%) for NO2 and PM2.5 in winter (summer), respectively. The UCM (a single-layer urban canopy model) model illustrates good performance in summer, with MFBs of 25.61% for NO2 and 19.03% for PM2.5 , while NO2 and PM2.5 concentrations are overestimated in winter, with MFBs of 62.58% and 38.19%, respectively. In contrast, BEP (a multilevel urban canopy model)-modelled NO2 (MFB: 37.18%) and PM2.5 (MFB: 18.72%) correlate well with observations in winter, while significantly overestimated air pollutant concentrations in summer with MFBs of NO2 and PM2.5 of 49.70% and 44.50%, respectively. In general, the BEP model and the BEM model are well suited for air quality simulations over Chengdu in winter, and the BEM model could be considered for air quality simulations in summer. Furthermore, we assessed the effects of extensive usage of air conditioning systems in Chengdu during summertime, and the results suggest that using air conditioning systems facilitates the dispersion of air pollutants over Chengdu. This study pinpoints the limitations of default WRF configurations and tests the applicability of urban canopy models in the WRF-CMAQ model over Chengdu, in addition highlighting the crucial role of urban canopy models in urban meteorological-air quality simulations.