In this study, observational data and WRF model simulations are used to investigate the characteristics and predictability of wind reversals during the morning and evening transition periods in a small valley with weakly complex terrain in the Blue Ridge Mountains of Virginia, USA. Focusing on 'valley wind days', the analysis focuses on the timing of wind reversals relative to surface heating and cooling, while also considering the influence of local terrain features, including a small hill located near the observation site. WRF simulations reproduce the observed flow conditions and demonstrate the utility of the vertical temperature difference between 2 and 10 m AGL, which may be used as a proxy for surface heating and cooling. A complete wind direction reversal occurs only when the temperature difference exceeds a consistent threshold. Terrain sensitivity experiments reveal that especially the downslope wind over the valley floor is modulated by local terrain effects, including those from interactions with a small hill and the western sidewall. The study highlights how numerical simulations can provide diagnostic insights to observed wind directions and their reversal times during morning and evening transition in a small, topographically complex valley.

East Asia faces increasing environmental risks under climate change and intensifying human activities. The region is dominated by the East Asian monsoon, which is driven by land-sea thermal contrast. Regional conditions are further shaped by strong interactions among climate, air pollution, ecosystem dynamics, and human activities. Understanding these cross-sphere processes and their associated risks requires high-resolution models that explicitly represent key mechanisms. Regional Earth system models address this need by providing higher resolution and more detailed process representations than global models. This review summarizes the development of the Regional Integrated Earth System Model (RIEMS), led by the Institute of Atmospheric Physics, Chinese Academy of Sciences, in collaboration with Nanjing University and other institutions. We describe RIEMS's evolution from a regional climate model to a comprehensive Earth system model, emphasizing key advances and the evaluation of its latest version, RIEMS 3.0. The development of RIEMS has progressed through three major stages. Early versions (RIEMS 1.0 and 2.0) established the foundation by integrating land surface processes, ocean components, and atmospheric chemistry with regional atmospheric dynamics. RIEMS 2.0 represented a major step forward by adopting a non-hydrostatic framework (Mesoscale Model 5 version 3; MM5v3) and incorporating spectral nudging, which effectively reduced large-scale circulation drift in long-term integrations. It also integrated the Atmosphere-Vegetation Interaction Model (AVIM) and online aerosol chemistry, enabling robust simulations of vegetation-climate feedbacks and aerosol-monsoon interactions. RIEMS 3.0 marks a shift toward a fully coupled "Atmosphere-Ocean-Land-Human" system. A key advance is the implementation of non-flux-adjusted coupling between the atmospheric component (Weather Research and Forecasting model version 4; WRF v4) and the ocean component (LASG/IAP Climate Ocean Model; LICOM-np) through the Ocean Atmosphere Sea Ice Soil version 3 (OASIS3) coupler. This configuration ensures rigorous conservation of energy and mass across the air-sea interface, substantially improving the representation of critical regional features such as the Western Pacific Subtropical High and tropical cyclone precipitation. To explicitly represent human influences, RIEMS 3.0 incorporates terrestrial carbon and nitrogen (CN) biogeochemical cycles into its land surface schemes (NoahMP-CN and AVIM-CN), enabling dynamic assessment of ecosystem responses to fertilization and nitrogen deposition. In addition, it includes an advanced urban canopy model to represent anthropogenic heat and impervious-surface effects, together with a multi-source satellite data assimilation system for initializing land surface states. Long-term experiments (1991-2014) demonstrate that RIEMS 3.0 effectively reduces systematic biases. For example, it lowers the root-mean-square error of 2-m air temperature over eastern China to approximately 1.0 K, outperforming both standalone WRF simulations and the CMIP6 multi-model mean. In the MICS-Asia III intercomparison, RIEMS 3.0 shows leading performance among participating models in simulating PM2.5 concentrations during severe pollution events in the Beijing-Tianjin-Hebei region. Looking ahead, development of the next-generation RIEMS 4.0 is underway to support national carbon neutrality strategies. Future work will focus on three strategic directions: (1) extending terrestrial biogeochemistry to include phosphorus (P) cycling, enabling complete C-N-P interactions to refine estimates of ecological carbon sinks; (2) building a seamless "land-river-ocean" continuum to simulate the transport of water, nutrients, and pollutants from terrestrial sources to coastal oceans, thereby clarifying mechanisms underlying coastal hypoxia and acidification; and (3) strengthening bidirectional coupling between atmospheric chemistry and physics to better resolve aerosol-cloud-radiation interactions. RIEMS 4.0 also aims to leverage artificial intelligence-through machine-learning parameterizations and differentiable modeling-to improve computational efficiency and predictive skill, providing a robust scientific basis for climate adaptation and sustainable development in East Asia.

In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM2.5 (particulate matter with an aerodynamic equivalent diameter of <= 2.5 mu m) predictions. This study compared the optimization effects of these two methods and developed a new scheme that combines DA and BC simultaneously. Four parallel experiments were conducted during winter 2019: a control experiment directly forecasted by WRF-Chem (experiment name: WRF-Chem); an experiment that assimilated in situ observations based on the GSI (Gridpoint Statistical Interpolation) system (WRF-Chem_DA); an experiment with deep-learning-based BC (WRF-Chem_BC); and an experiment considering the combination of DA on the initial conditions and BC (WRF-Chem_DA_BC). Statistically, the accuracy of PM2.5 predictions could be optimized by both DA and BC for the first 24-h period, and WRF-Chem_BC performed better than WRF-Chem_DA in the initial field, especially in the period of 10-24 h, while the best performance was achieved by combining BC and DA. Throughout the initial 24-h period, compared with the control experiment, the results of WRF-Chem_DA_BC (WRF-Chem_DA, WRF-Chem_BC) showed an improvement in terms of root-mean-square error, with reduction proportions varying from 38.90 % to 48.86 % (18.88 % to 32.44 %, 30.10 % to 46.08 %). Besides having the best optimization effect over the whole domain, the combined method also performed well in different regions: during the forecasting period of 0-24 h, the RMSEs decreased from 32 % to 62 %, 39 % to 57 %, 28 % to 40 %, and 30 % to 49 % in the Beijing-Tianjin-Hebei, Yangtze River Delta, Central China, and Sichuan Basin urban agglomerations, respectively.

Accurate predictions of atmospheric particulate matter can be applied in providing services for air pollution prevention and control. However, the forecasting accuracy of traditional air quality models is limited owing to model uncertainties. In this study, we developed a deep learning model, named multiscale depth-separable UNet (MDS-UNet), to improve PM2.5 and PM10 concentration forecasts from WRF_Chem over China. Results showed that MDS-UNet was able to capture the complex nonlinear errors between model predictions and observations, which was helpful in correcting the biases and spatiotemporal distribution patterns of PM2.5 and PM10 concentrations predicted by WRF_Chem. MDS-UNet made a better performance in the improvement of both PM2.5 and PM10 prediction accuracy than UNet and CNN during the 0-24 forecasts. Using MDS-UNet, the reductions in the root-mean-square error (RMSE) of the regionally averaged PM2.5 and PM10 concentration forecasts were 35.08% and 17.74%, respectively. During the 0-24-h forecast period, MDS-UNet performed well in terms of PM2.5 and PM10 over six key urban agglomerations in China. Taking a pollution process as a case study, results demonstrated that, compared with WRF_Chem, MDS-UNet was able to make the best improvement in YRD, the Sichuan Basin, and central China, with reductions in the RMSE of the PM2.5 forecasts of 55.22%, 55.53%, and 52.17%, respectively; and for PM10 forecasts these reductions were 44.90%, 40.97%, and 46.79%, respectively. Through this analysis, it was apparent that MDS-UNet demonstrated a better effect in terms of improving both PM2.5 and PM10 predictions in these key urban agglomerations during an important pollution process.

Local air pollution is strongly affected by synoptic weather systems, such as fronts, troughs, low-altitude vortices, or high-altitude ridges. Nevertheless, few studies have analyzed the meteorological properties of cold or warm air masses associated to these systems and their impact on local air quality. In this study, hourly observations of fine particulate matter (diameter of up to 2.5 µm, i.e., PM2.5), wind (V), temperature (T), pressure (P), and precipitation (R), acquired in Hangzhou in 2014–2020, were analyzed. From this analysis, weather patterns were categorized into 27 types; 89 and 94 cases illustrating the passage of warm and cold air masses over Hangzhou were identified, respectively; the influence of air mass temperature, wind speed, and wind direction on PM2.5 concentrations and local accumulation or removal was quantified. The main results are as follows. (1) Pollution events occurred more frequently for cold than for warm air masses, but average pollutant concentration was lower for cold air masses; (2) 48

Rapid urbanization and industrialization in China have resulted in an increase of PM2.5 concentrations. In this study, an interpretable multi-model stacking ensemble method (IMSEM) with top-of-the-atmosphere reflectance (TOAR) from the Himawari-8 satellite were used to acquire high-resolution PM2.5 data in China. In contrast to the traditional approach whereby PM2.5 is estimated with single models, using TOAR data, IMSEM outperformed single models in terms of several skill scores. The hourly average R2(RMSE) of 10-fold-cross validation reached 0.84 (9.52 μg/m3) in 2021 by IMSEM. The feature importance results of IMSEM showed the significant contributions of TOAR and meteorological variables. The PM2.5 estimates of IMSEM were also fused with surface observations using interpolation for correction and optimization. When this was done for PM2.5 concentrations in 2022, it was found that, among the four seasons, the fusion-based estimate of PM2.5 concentration was highest in winter (49.94 μg/m3), followed by autumn (31.59 μg/m3) and spring (29.07 μg/m3), and lowest in summer (19.25 μg/m3).

Continuous improvement in remote sensing observation-based techniques has enabled data inversion assimilation methods to play an increasingly important role in the field of numerical forecasting. Using an artificial intelligence fusion inversion method can overcome the multisource heterogeneity of data, thereby providing a better blended source for data assimilation (DA) and improving the forecasting capability. This study proposed a multimodel stacking machine learning algorithm to estimate surface concentrations of PM2.5 (particulate matter with an aerodynamic equivalent diameter of <= 2.5 mu m) using Himawari-8 satellite data. The derived satellite-based PM2.5 estimation was then blended with the in situ observations as physical constraints to obtain a high-resolution multisource blended dataset. To determine the level of improvement provided by the blended dataset, four parallel experiments were conducted during February 2022: a control experiment without DA (noDA), an experiment that assimilated satellite-based estimation dataset (sate-basedDA), an experiment that assimilated multisource blended dataset (blendDA) and an experiment that assimilated in situ observations (siteDA) based on the Gridpoint Statical Interpolation system. Statistically, in comparison with the direct satellite-based PM2.5 estimations, the blended dataset better matched the in situ observations. The accuracy of PM2.5 predictions can be optimized by DA and for the first 24-h period the blended dataset performed better than the traditional ground observations as the assimilation source. Throughout the initial 24-h period, the results of blendDA (siteDA, sate-basedDA) showed an improvement in terms of root-mean-square error, with reductions varying from 6.65 to 20.20 mu g/m(3) (4.50-19.79 mu g/m(3), 2.29-10.86 mu g/m(3)).

WRF-Chem was used to study a severe haze episode that occurred over the Yangtze River Delta (YRD), China, in November 2013. This episode was characterized by a high PM2.5 concentration (> 400 µg m−3), high relative humidity (> 80

Statistical post-processing of systematic errors is required for numerical weather predictions to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with different individual models and for one specific element of focus. Here, a promising new method is proposed for the post-processing of meteorological elements output by the European Centre for Medium-Range Weather Forecasts (ECMWF), based on the integration of several different models. For 24-h precipitation, 2-m temperature, and 10-m wind speed, our new method, called the Multimodel Integration Embedded Method (MMIEM), outperformed the single models in terms of several skill scores, while being computationally more convenient. The mean average error of the MMIEM post-processed daily maximum 2-m temperature, minimum 2-m temperature, and maximum 10-m wind speed, was 18%, 26% and 29% lower than that of the raw ECMWF forecast, respectively. Also, compared with ECMWF, the threat score of the rainstorm forecast was improved by 9%. Key attributions to this improvement were the use of multiple models containing different advantages, with help from the embedding process and the interaction of multiple features in the model training. Furthermore, MMIEM can be extended to other statistical and forecast problems. It is anticipated that, with ever-increasing amounts of data, machine learning methods can transform the post-processing of numerical weather forecasts by gaining insight into the importance of different meteorological elements.

Vegetation has always been an integral part of the urban scene, affecting ambient air quality through both direct and indirect ways: by enhancing the dry deposition process of air pollutants and by contributing to the formation of ozone due to the emission of biogenic volatile organic compounds (BVOC). In this study, hourly measurements of gaseous dry deposition velocities are used to evaluate the performance of two dry deposition modules. Based on verification against measurements, an online coupled modeling system (RBLM-Chem) is introduced to investigate the dry deposition process of air pollutants (both gas and particles), the diurnal and seasonal variation patterns, and the discrepancy between different vegetation species as well as to assess the role of urban vegetation in affecting local air quality under different greening scenarios. Results indicate that trees are generally more efficient in removing air pollutants than shorter vegetation (e.g., grass). Moreover, conifers exhibit higher dry deposition velocities than broadleaf trees in terms of annual average. The introduction of vegetation (either trees or grass) clearly raises the dry deposition velocity of air pollutants. The air pollutant that is most removed by urban vegetation in Suzhou is PM10, with an annual removal rate of 1484.5 t a−1. The current urban greening scenario within Suzhou contributes to a reduction in daily mean concentration of 8.1

Many efforts have been made to control PM2.5 pollution in China during the National 13th Five-Year Plan and PM2.5 concentrations significantly declined nationwide. Meteorological conditions and pollution emissions play the most important roles in PM2.5 pollution. However, extant quantitative estimations of the contributions of meteorological conditions and anthropogenic emission reductions during the period were mostly based on surface observations data. Using a reanalysis dataset from 2016 to 2020, our results reveal that the annual average PM2.5 concentrations have been reduced yearly in the four key regions: the Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD), Pearl River Delta (PRD) and central China. The frequency of stagnation days decreased in 2020 in the YRD and PRD, while it did not show significant fluctuations in the BTH region and central China. Through the sensitivity simulation experiments in 2016 and 2020, the implementation of the emission regulations led to PM2.5 pollution reductions of 6.59 and 5.12 mu g m  3, respectively in the BTH region and central China, of which the worsening meteorological conditions offset 61.46% (4.05 mu g m  3) and 19.53% (1.00 mu g m  3) respectively. However, the impacts of favorable weather conditions and effective anthropogenic restrictions jointly contributed to the improvement in air quality in the YRD and PRD. Anthropogenic controls explained 81.95% (3.75 mu g m  3) of the total 4.58 mu g m  3 reduction in the YRD and 52.10% (1.86 mu g m  3) of the total 3.57 mu g m  3 reduction in the PRD. The findings provide a perspective on the causes of pollution events and would help formulate more effective emission reduction policies in the future.

The Weather Research and Forecasting model was employed to simulate the effects of cooling roofs on the urban heat island effect and human thermal stress in the Pearl River Delta region, China, in summer (June-August) 2014. It was found that cooling roofs can reduce the 2-m air and ground-surface temperature, wind speed, and planetary boundary layer height, while increasing the specific and relative humidity, in urban areas. The effects can influence the entire urban boundary layer, especially in commercial/industrial areas in the daytime. Cooling roofs can effectively mitigate the urban heat island effect, and the mitigation of white roofs is more significant than that of green roofs. The mitigation of the urban heat island effect is stronger in commercial/industrial areas than in low-intensity residential areas. The city-wide 2-m and ground-surface urban heat island intensity induced by white roofs can reduce by 1.5 degrees C and 4.8 degrees C at noon, while the corresponding decreases induced by green roofs can reach 0.7 degrees C and 1.9 degrees C, respectively. White roofs improve human thermal comfort both in the daytime and at nighttime. With white roofs, three city-wide thermal-stress indices (wet-bulb globe temperature, apparent temper-ature, and humidity index) can be reduced at noon by 0.8 degrees C, 1.3 degrees C and 1.5 degrees C, respectively. Green roofs mostly improve human thermal comfort at nighttime but even deteriorate it in the daytime. The above three indices decrease by less than 0.5 degrees C in different urban categories at nighttime with green roofs.

沿海城市的PM2.5和臭氧除受排放源、天气条件影响以外,还往往同时受城市热岛环流和海陆风环流的双重影响.利用2015年杭州市气象和环境监测数据以及数值模式RBLM-Chem,分析研究了杭州市在陆风天气、海风天气和海陆风三种环流条件下污染物浓度特征及城市效应对其的影响.得到了以下主要结论:海风使杭州市污染物浓度增大,在观测数据中PM2.5浓度和臭氧浓度分别最大增高了 10.9μg·m-3和12.0μg·m-3,在模拟结果中相比于陆风天气型,海陆风天气型的PM2.5浓度和臭氧浓度分别增大13.1μg·m-3和18.9μg·m-3;相比于海风天气型,海陆风天气型的PM2.5浓度和臭氧浓度分别减小24.1μg·m-3和11.6 μg·m-3.城市效应导致杭州市边界层高度增加63.8 m,地面风速减小0.99 m·s-1,地面气温增高1.14℃,PM2.5浓度增大2.86 μg·m-3,臭氧浓度增大10.2μg·m-3.海风削弱了杭州的城市效应,城市对边界层高度、地面风速、地面气温和臭氧浓度的影响分别减小11.2 m,0.49 m·s-1,0.26 ℃和7％.

Impacts of coastal local circulations and their interactions on ozone (O3) in Hangzhou on 5 June 2018 with relatively low temperatures were investigated using surface observations and three-dimensional air quality simulations. Local circulations and their interactions were primary causes of the episode instead of high temperatures. Northwesterly land breeze on the previous day transported O3 to sea, and sea breeze (SB) on the episode day blew pollution back to land. Inland penetration (east to west) of the combined SB front (SBF) was accelerated by urban heat island (UHI) induced easterly flow over coast and slowed by its westerly flow and surface roughness over inland urban areas. O3 accumulation at surface and in the elevated air over city was caused by mountain barrier effect and venting process. Subsidence heating associated with valley circulation accelerated UHI circulation (UHIC) before 1400 LT, indirectly strengthening the SB circulation (SBC). The UHIC and valley circulation decoupled with SBC at noon and in the late afternoon, respectively, leading to a stronger SBC. The stronger SBC brought additional O3 (>20 ppb) to further inland area, to elevated air with updrafts in SB head, and to nocturnal stable boundary layer and residual layer with its upper-level offshore flow.

Urbanization has promoted economic growth but it can increase gust wind speed, which may lead to serious damage to infrastructures. This study uses the Weather Research and Forecasting model and a gust parametrization scheme to evaluate the mitigation impact of white roofs and green roofs on wind gust over the Pearl River Delta, an urban agglomeration in Southern China in June, July, and August of 2014. The results show that both white and green roofs decrease the gust wind speed by decreasing the mean wind speed, suppressing the turbulent motion and weakening the convection. The impacts of white roofs are stronger than those of green roofs. The daily mean reductions of gust wind speed are approximately 1.2–1.3 m s ^−1 (12%–16%) and 0.4–0.6 m s ^−1 (6%–10%) by white and green roofs, respectively. In general, the contribution of turbulence (60%–85%) to the gust wind speed is the largest, and the contribution of mean wind speed is approximately 10%–30%, however, the effect of deep convection is not obvious (0%–15%) on the decrease of gust wind speed. The effect of cooling roofs on reducing the gust wind speed is stronger during daytime than during nighttime, and the effect is more significant in city areas that have higher building densities. Based on the findings, this study is potentially beneficial for policy-makings in developing urban disaster mitigation methods.

Previous studies have extensively examined effects of urbanization on mean wind speed, but few studies were aimed at gust wind speed, while large wind gust could cause safety and economic hazard to a variety of activities. In this study, the effect of urbanization on the gust wind speed in Nanjing, China is assessed using the Weather Research and Forecasting (WRF) model with a parameterization of the gust wind speed. The WRF simulations are run for the summer period of 2013 with the underlying surface before and after the urbanization. The results indicate that although the mean wind speed is reduced, the gust wind speed in the urban areas is increased significantly due to the enhanced friction velocity and less atmospheric stability induced by the urbanization, while the contribution of deep convection is relatively small. The gust wind speed increases more in the nighttime (0.6–0.9 m s −1 ) than in the daytime (less than 0.3 m s −1 ), since the turbulence is enhanced more in the nighttime than in the daytime after the urbanization. The probability distribution shows that the increase of gust wind speed is mainly between 0.0–0.5 m s −1 in the urban areas. In different urban land categories, the increase of the gust wind speed is larger in the commercial or industrial areas than in high-intensity and low-intensity residential urban areas. Averagely, the gust wind speed in the entire city after the urbanization increases by 0.02, 0.36 and 0.19 m s −1 for the daytime, nighttime and daily mean, respectively.

Aerosols could affect the thermal radiation process of planetary boundary layer (PBL), thus changing its thermodynamic and circulation structure and further triggering changes in aerosol concentrations. However, previous studies mostly focused on the direct health impacts of aerosols and meteorological conditions in the boundary layer and little is known about the health impacts associated with aerosol-PBL interactions. We quantitatively estimate the impacts of PM2.5 and air temperature on mortality caused by aerosol-PBL interactions. Utilizing the WRF-Chem model, we conducted a one-month numerical simulation during December 2015 in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions with the highest population density in China. We simulated PM2.5 concentrations and air temperature under two scenarios: with/without considering the aerosol-PBL interactions. Based on the simulation data, we adopted the distributed lag non-linear model (DLNM) to establish the exposure-response associations of PM2.5 and temperature with mortality and calculated the population-weighted exposure levels of PM2.5 and temperature. During December 2015 in the BTH region, the additional PM2.5-related and temperature-related deaths were 312 due to respiratory and circulatory disease. Without considering the aerosol-PBL interactions, the population-weighted average levels of both PM2.5 and temperature increased compared with the average levels. However, after considering the aerosol-PBL interactions, the population-weighted average temperature was decreased, while the population-weighted average PM2.5 concentration was increased. In the BTH region, more people lived in areas with high PM2.5 concentrations where have strong aerosol-PBL interactions. The aerosol-PBL interactions could intensify the adverse health impacts of temperature and PM2.5, which should not be neglected when to assess the health effects of aerosol and meteorological conditions.

This observational study investigates New York City (NYC) impacts on summer sea breeze fronts (SBFs) during a 2018 LISTOS Campaign day with a regional heat wave and O-3 episode. A morning urban heat island peaked at 8.3 degrees C and then induced convergences into the City, trapping its NO2 emissions. SBFs came ashore at 0700 EST from the Atlantic along southern Long Island (LI), and from the LI Sound along northern LI and southern Connecticut; 2-h later another formed over New Jersey. The Ocean front was retarded over NYC at noon, while all fronts merged by 1400 EST and continued inland for four more hours. High O-3 first appeared at 0900 EST downwind of NYC. By 1100 EST, a new surface peak formed north of the City in the Hudson River Valley (HRV). The maxima merged, peaking at 143 ppb at 1300 EST behind the SBF and near the maximum temperatures of 39 degrees C. Trajectories ending at the northern LI site with a PBL O-3 peak first passed NYC, arrived before the episode, and then recirculated back in its SB flow. Trajectories ending in the HRV showed pollutant transport over NYC twice, before advection northward into the narrow Valley by the ocean SBF.

Based on the visibility observation data of Nanjing Lukou Airport from 2014 to 2019, this paper makes a statistical analysis of the low visibility weather at the airport. It was found that low visibility weather mostly occurred between 04:00 BST and 07:00 BST from November to January. The relationship between rainfall intensity and visibility was found to be a power function by analyzing the rainfall intensity-visibility data, and the visibility fitting model based on rainfall intensity is established. Through K-means cluster analysis, it was found that the relationship between visibility and wind speed, as well as visibility and temperature were positive, and the relationship between visibility and relative humidity, as well as visibility and sea level pressure were negative in the absence of precipitation. Finally, the low visibility weather level judgment model was established by using the random forest algorithm. Through comparative analysis, we also found that the model established based on random forest algorithm had certain advantages over other models with respect of accuracy and computational efficiency.

使用RBLM-Chem模式,利用杭州市高分辨率城市建筑资料,通过敏感性试验的方法,定量分析城市植被的直接环境效应.结果表明,城市植被对大气污染物干沉降速率总体上起增加作用.夏季城市植被使城区SO2、NO2、O3、PM2.5的干沉降速率分别增加0.15 cm/s、0.10 cm/s、0.02 cm/s、0.05 cm/s,冬季该作用不太明显.城市植被对大气污染物干沉降速率的增加作用白天显著大于夜晚.城市植被可以显著降低城市地区大气污染物浓度,对不同大气污染物浓度的下降作用强弱在昼夜的分布依物种而异.