scholarly journals Effects of aerosol–radiation interaction on precipitation during biomass-burning season in East China

2016 ◽  
Vol 16 (15) ◽  
pp. 10063-10082 ◽  
Author(s):  
Xin Huang ◽  
Aijun Ding ◽  
Lixia Liu ◽  
Qiang Liu ◽  
Ke Ding ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Biomass Burning ◽  
Convective Precipitation ◽  
East China ◽  
Small Scale ◽  
Surface Cooling ◽  
Weather Modification ◽  
Near Surface ◽  
Carbonaceous Particles

Abstract. Biomass burning is a main source for primary carbonaceous particles in the atmosphere and acts as a crucial factor that alters Earth's energy budget and balance. It is also an important factor influencing air quality, regional climate and sustainability in the domain of Pan-Eurasian Experiment (PEEX). During the exceptionally intense agricultural fire season in mid-June 2012, accompanied by rapidly deteriorating air quality, a series of meteorological anomalies was observed, including a large decline in near-surface air temperature, spatial shifts and changes in precipitation in Jiangsu province of East China. To explore the underlying processes that link air pollution to weather modification, we conducted a numerical study with parallel simulations using the fully coupled meteorology–chemistry model WRF-Chem with a high-resolution emission inventory for agricultural fires. Evaluation of the modeling results with available ground-based measurements and satellite retrievals showed that this model was able to reproduce the magnitude and spatial variations of fire-induced air pollution. During the biomass-burning event in mid-June 2012, intensive emission of absorbing aerosols trapped a considerable part of solar radiation in the atmosphere and reduced incident radiation reaching the surface on a regional scale, followed by lowered surface sensible and latent heat fluxes. The perturbed energy balance and re-allocation gave rise to substantial adjustments in vertical temperature stratification, namely surface cooling and upper-air heating. Furthermore, an intimate link between temperature profile and small-scale processes like turbulent mixing and entrainment led to distinct changes in precipitation. On the one hand, by stabilizing the atmosphere below and reducing the surface flux, black carbon-laden plumes tended to dissipate daytime cloud and suppress the convective precipitation over Nanjing. On the other hand, heating aloft increased upper-level convective activity and then favored convergence carrying in moist air, thereby enhancing the nocturnal precipitation in the downwind areas of the biomass-burning plumes.

scholarly journals Effects of aerosol-radiation interaction on precipitation during biomass-burning season in East China

2016 ◽  
Author(s):  
Xin Huang ◽  
Aijun Ding ◽  
Lixia Liu ◽  
Qiang Liu ◽  
Ke Ding ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Biomass Burning ◽  
Convective Precipitation ◽  
East China ◽  
Small Scale ◽  
Surface Cooling ◽  
Weather Modification ◽  
Near Surface ◽  
Carbonaceous Particles

Abstract. Biomass burning is a main source for primary carbonaceous particles in the atmosphere and acts as a crucial factor that alters Earth's energy budget and balance. It is also an important factor influencing air quality, regional climate and sustainability in the domain of Pan-Eurasian Experiment (PEEX). During the exceptionally intense agricultural fire season in mid-June 2012, accompanied with rapidly deteriorating air quality, a series of meteorological anomalies was observed, including a large decline in near-surface air temperature, spatial shifts and changes in precipitation in Jiangsu Province of East China. To explore the underlying processes that link air pollution to weather modification, we conducted a numerical study with parallel simulations using the fully coupled meteorology-chemistry model WRF-Chem with a high-resolution emission inventory for agricultural fires. Evaluation of the modelling results with available ground-based measurements and satellite retrievals showed that this model was able to reproduce the magnitude and spatial variations of fire-induced air pollution. During the biomass-burning event in mid-June 2012, intensive emission of absorbing aerosols trapped a considerable part of solar radiation in the atmosphere and reduced incident radiation reaching the surface on a regional scale, followed by lowered surface sensible and latent heat fluxes. The perturbed energy balance and re-allocation gave rise to substantial adjustments in vertical temperature stratification, namely surface cooling and upper-air heating. Furthermore, intimate link between temperature profile and small-scale processes like turbulent mixing and entrainment led to distinct changes in precipitation. On one hand, by stabilizing the atmosphere below and reducing the surface flux, black carbon-laden plumes tended to dissipate daytime cloud and suppress the convective precipitation over Nanjing. On the other hand, heating aloft increased upper-level convective activity and then favored convergence carrying in moist air, thereby enhancing the nocturnal precipitation in the downwind areas of the biomass burning plumes.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

scholarly journals The impacts of biomass burning activities on convective systems over the Maritime Continent

2020 ◽  
Vol 20 (4) ◽  
pp. 2533-2548
Author(s):  
Hsiang-He Lee ◽  
Chien Wang
Keyword(s):  
Biomass Burning ◽  
Cloud Condensation Nuclei ◽  
Radiation Budget ◽  
Convective Precipitation ◽  
Fire Season ◽  
Maritime Continent ◽  
Near Surface ◽  
Anticyclonic Circulation ◽  
Convective Systems ◽  
Aerosol Cloud Interactions

Abstract. Convective precipitation associated with Sumatra squall lines and diurnal rainfall over Borneo is an important weather feature of the Maritime Continent in Southeast Asia. Over the past few decades, biomass burning activities have been widespread during summertime over this region, producing massive fire aerosols. These additional aerosols, when brought into the atmosphere, besides influencing the local radiation budget through directly scattering and absorbing sunlight, can also act as cloud condensation nuclei or ice nuclei to alter convective clouds and precipitation over the Maritime Continent via so-called aerosol indirect effects. Based on 4-month simulations with or without biomass burning aerosols, conducted using the Weather Research and Forecasting model coupled with a chemistry module (WRF-Chem), we have investigated the aerosol–cloud interactions associated with biomass burning aerosols over the Maritime Continent. Results from selected cases of convective events have specifically shown the significant impact of fire aerosols on weak convections by their increasing of the quantities of hydrometeors and rainfall in both the Sumatra and Borneo regions. Statistical analysis over the fire season also suggests that fire aerosols have impacts on the nocturnal convections associated with the local anticyclonic circulation in western Borneo and weaken nocturnal rainfall intensity by about 9 %. Such an effect is likely to have come from the near-surface heating due to absorbing aerosols emitted from fires, which could weaken land breezes and thus the convergence of anticyclonic circulation.

scholarly journals A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

2017 ◽  
Author(s):  
Dixian Zhu ◽  
Changjie Cai ◽  
Tianbao Yang ◽  
Xun Zhou
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Small Scale ◽  
Quality Prediction ◽  
Pollution Concentration ◽  
Task Learning ◽  
Air Quality Prediction ◽  
Standard Regression

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

scholarly journals Mapping Anisotropic Landscape for Understanding Underlying Spatial Structure of Air Pollution

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Chen-Hsuan Tu ◽  
Tzai-Hung Wen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Statistical Approach ◽  
Space Time ◽  
Grid Resolution ◽  
Sensor Data ◽  
Small Scale ◽  
Physical Factors ◽  
Physical Interactions ◽  
Time Grid

<p><strong>Abstract.</strong> Urban air pollution problem has become a huge threat to human health in the most developing and developed countries. Therefore, monitoring air quality with high spatial and temporal resolutions is an important issue. There are two different approaches to mapping street-level distributions of air quality in time and space. One is mathematical approach, which uses numerical methods to calculate the concentration of air pollutants in each space-time grid through considering chemical transport, wind field, terrain morphology and other parameters which affect the direction and intensity of dispersion. This approach is limited by intensively computational process, so most of studies used either rough spatial grid resolution for representing large-scale regions or detailed grid resolution for small-scale areas. Numerical models with rough grid resolution could not capture detailed physical interactions in the micro-environment. The other approach is statistical approach, which used spatial interpolation techniques, such as inverse distance weighting (IDW) and Kriging methods, or established regression models, such as land-use regression (LUR), for deriving concentrations of air pollution from remote sensing or ground-level station sensor data. This approach is assumed linear associations with environmental factors and isotropic distance-decayed phenomena, which also ignores complex physical interactions.</p><p>Spatial distribution of air pollution could be affected by directional background factors, such as wind fields, surface relief and so on. The spatial effects of these physical factors are not isotropic. However, recent studies used statistical modelling approaches are based on isotropic assumptions and did not consider directional variations of these factors on air quality. The purpose of the study is to develop an innovative statistical approach to measure directional effects on air quality with spatial heterogeneity. We produces anisotropic landscapes of directional fields for identifying major directions for each space-time grid through EPA’s monitoring station data to visualize space-time trend of air quality changing with directions. This study provides significant insight for understanding spatial structures behind air pollution distributions influenced by directional physical factors.</p>

scholarly journals Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

2020 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Kirsten Warrach-Sagi ◽  
Thomas Bönisch ◽  
Volker Wulfmeyer
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Urban Areas ◽  
Single Layer ◽  
Urban Canopy ◽  
Surface Fluxes ◽  
Residential Areas ◽  
Near Surface ◽  
Air Quality Forecast

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations at European courts. Information on the level of air pollution is based on near surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project Open Forecast. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence permitting resolution of 50 m were used and a single layer urban canopy model was active in all domains. As demonstration case study the 21 January 2019 was selected which was a heavy polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is capable to reasonably simulate the diurnal cycle of surface fluxes and 2-m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and Nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecast and has the potential of being applied to other cities around the world.

scholarly journals Remote biomass burning dominates southern West African air pollution during the monsoon

2019 ◽  
Vol 19 (24) ◽  
pp. 15217-15234 ◽  
Author(s):  
Sophie L. Haslett ◽  
Jonathan W. Taylor ◽  
Mathew Evans ◽  
Eleanor Morris ◽  
Bernhard Vogel ◽  
...  
Keyword(s):  
Air Pollution ◽  
West Africa ◽  
Atlantic Ocean ◽  
Biomass Burning ◽  
West African ◽  
Small Scale ◽  
High Background ◽  
Accumulation Mode ◽  
Aerosol Loading ◽  
Biomass Burning Aerosol

Abstract. Vast stretches of agricultural land in southern and central Africa are burnt between June and September each year, which releases large quantities of aerosol into the atmosphere. The resulting smoke plumes are carried west over the Atlantic Ocean at altitudes between 2 and 4 km. As only limited observational data in West Africa have existed until now, whether this pollution has an impact at lower altitudes has remained unclear. The Dynamics-aerosol-chemistry-cloud interactions in West Africa (DACCIWA) aircraft campaign took place in southern West Africa during June and July 2016, with the aim of observing gas and aerosol properties in the region in order to assess anthropogenic and other influences on the atmosphere. Results presented here show that a significant mass of aged accumulation mode aerosol was present in the southern West African monsoon layer, over both the ocean and the continent. A median dry aerosol concentration of 6.2 µg m−3 (standard temperature and pressure, STP) was observed over the Atlantic Ocean upwind of the major cities, with an interquartile range from 5.3 to 8.0 µg m−3. This concentration increased to a median of 11.1 µg m−3 (8.6 to 15.7 µg m−3) in the immediate outflow from cities. In the continental air mass away from the cities, the median aerosol loading was 7.5 µg m−3 (5.9 to 10.5 µg m−3). The accumulation mode aerosol population over land displayed similar chemical properties to the upstream population, which implies that upstream aerosol is a significant source of aerosol pollution over the continent. The upstream aerosol is found to have most likely originated from central and southern African biomass burning. This demonstrates that biomass burning plumes are being advected northwards, after being entrained into the monsoon layer over the eastern tropical Atlantic Ocean. It is shown observationally for the first time that they contribute up to 80 % to the regional aerosol loading in the monsoon layer over southern West Africa. Results from the COSMO-ART (Consortium for Small-scale Modeling – Aerosol and Reactive Trace gases) and GEOS-Chem models support this conclusion, showing that observed aerosol concentrations over the northern Atlantic Ocean can only be reproduced when the contribution of transported biomass burning aerosol is taken into account. As a result, the large and growing emissions from the coastal cities are overlaid on an already substantial aerosol background. Simulations using COSMO-ART show that cloud droplet number concentrations can increase by up to 27 % as a result of transported biomass burning aerosol. On a regional scale this renders cloud properties and precipitation less sensitive to future increases in anthropogenic emissions. In addition, such high background loadings will lead to greater pollution exposure for the large and growing population in southern West Africa. These results emphasise the importance of including aerosol from across country borders in the development of air pollution policies and interventions in regions such as West Africa.

scholarly journals Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

2021 ◽  
Vol 21 (6) ◽  
pp. 4575-4597
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Kirsten Warrach-Sagi ◽  
Thomas Bönisch ◽  
Volker Wulfmeyer
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Urban Areas ◽  
Single Layer ◽  
Urban Canopy ◽  
Surface Fluxes ◽  
Residential Areas ◽  
Near Surface ◽  
Air Quality Forecasting

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations in European courts. Information on the level of air pollution is based on near-surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project “Open Forecast”. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence-permitting resolution of 50 m were used, and a single-layer urban canopy model was active in all domains. As a demonstration case study, 21 January 2019 was selected, which was a heavily polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is able to reasonably simulate the diurnal cycle of surface fluxes and 2 m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecasting and has the potential of being applied to other cities around the world.

scholarly journals MODELS DESCRIBING THE ENVIRONMENTAL EFFECTS OF POLLUTANT EMISSIONS BY ROAD TRANSPORT

2021 ◽  
Author(s):  
Xindi Huang ◽  
Nadezhda Yudina
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Gaussian Model ◽  
Pollutant Emissions ◽  
World Health ◽  
Bottom Surface ◽  
Small Scale ◽  
Forecasting Model ◽  
Transfer Model ◽  
The World

Air pollution is the most serious environmental problem facing most industrial cities in the world and in China. The World Health Organization measured the concentration of sulfur dioxide, nitrogen dioxide and total suspended particulate matter in 272 cities in 53 countries around the world, listing the ten most severely polluted cities in the world. The spatial and temporal distribu-tion of air pollutants depends on various factors such as the meteorological field, the source of emissions, the complex bottom surface of the site, the interplay of physical and chemical processes, and has strong non-linear characteristics [5]. Air quality forecasting is commonly used in the field of statistical forecasting methods, according to long-term monitoring data, the creation of a statisti-cal forecasting model, the model is simple, easy to operate business, but no solid physical founda-tion, and another numerical forecasting model based on atmospheric physics and material transfer model although the physical foundation is solid, comprehensive forecast results, but the forecast results are not reliable. Already in the 1950s, the system of meteorology of air pollution was gradu-ally formed, the box model, the Gaussian model, the Lagrange model, the Euler model, the dense gas model and other five types of models appeared. The first Gaussian model allows one to obtain a diffusion model of a local small-scale space and make predictions, then, based on the Gaussian model of the study, a modified model is obtained for other reliefs and weather conditions. There-fore, the modeling accuracy and applicable conditions are difficult to cope with the needs of large-scale complex meteorological conditions of air quality models.

scholarly journals Development of a high temporal–spatial resolution vehicle emission inventory based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality

2015 ◽  
Vol 15 (13) ◽  
pp. 19239-19273 ◽  
Author(s):  
J. J. He ◽  
L. Wu ◽  
H. J. Mao ◽  
H. L. Liu ◽  
B. Y. Jing ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Emission Inventory ◽  
Model Simulation ◽  
Pollutant Emissions ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Near Surface

Abstract. In a companion paper (Jing et al., 2015), a high temporal–spatial resolution vehicle emission inventory (HTSVE) for 2013 in Beijing has been established based on near real time (NRT) traffic data and bottom up methodology. In this study, based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modeling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing main urban areas in the periods of summer (July) and winter (December) 2013. Generally, CUACE model had good performance of pollutants concentration simulation. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes over moment. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO2, while 5.4 and 10.5 % for PM2.5 in July and December 2013, respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO2 is 59.2 and 57.8 % in July and December 2013, while 8.7 and 13.9 % for PM2.5. The RVEC to PM2.5 is lower than PM2.5 contribution rate for vehicle emission in total emission, which may be caused by easily dry deposition of PM2.5 from vehicle emission in near-surface layer compared to elevated source emission.

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