scholarly journals Dispersion of particulate matter (PM<sub>2.5</sub>) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations

2021 ◽  
Vol 21 (16) ◽  
pp. 12463-12477
Author(s):  
Tobias Wolf ◽  
Lasse H. Pettersson ◽  
Igor Esau
Keyword(s):  
Air Pollution ◽  
Spatial Distribution ◽  
Air Quality ◽  
Complex Terrain ◽  
Small Scale ◽  
Wood Combustion ◽  
Urban Districts ◽  
Large Eddy ◽  
Policy Scenarios

Abstract. Many cities in the world experience significant air pollution from residential wood combustion. Such an advection–diffusion problem as applied to geographically distributed small-scale pollution sources presently does not have a satisfactory theoretical or modeling solution. For example, statistical models do not allow for pollution accumulation in local stagnation zones – a type of phenomena that is commonly observed over complex terrain. This study applies a Parallelized Atmospheric Large-eddy simulation Model (PALM) to investigate dynamical phenomena that control variability and pathways of the atmospheric pollution emitted by wood-burning household stoves. The model PALM runs at spatial resolution of 10 m in an urban-sized modeling domain of 29 km by 35 km with a real spatial distribution of the pollution source and with realistic surface boundary conditions that characterize a medium-sized urban area fragmented by water bodies and hills. Such complex geography is expected to favor local air quality hazards, which makes this study of general interest. The case study here is based on winter conditions in Bergen, Norway. We investigate the turbulent diffusion of a passive scalar associated with small-sized particles (PM2.5) emitted by household stoves. The study considers air pollution effects that could be observed under different policy scenarios of stove replacement; modern woodstoves emit significantly less PM2.5 than the older ones, but replacement of stoves is a costly and challenging process. We found significant accumulation of near-surface pollution in the local stagnation zones. The simulated concentrations were larger than the concentrations obtained only due to the local PM2.5 emission, thus indicating dominant transboundary contribution of pollutants for other districts. We demonstrate how the source of critical pollution can be attributed through model disaggregation of emission from specific districts. The study reveals a decisive role of local air circulations over complex terrain that makes high-resolution modeling indispensable for adequate management of the urban air quality. This modeling study has important policy-related implications. Uneven spatial distribution of the pollutants suggests prioritizing certain limited urban districts in policy scenarios. We show that focused efforts towards stove replacement in specific areas may have a dominant positive effect on the air quality in the whole municipality. The case study identifies urban districts where limited incentives would result in the strongest reduction of the population's exposure to PM2.5.

scholarly journals Dispersion of particulate matter (PM<sub>2.5</sub>) from wood combustion for residential heating: Optimisation of mitigation actions based on large-eddy simulations

2021 ◽  
Author(s):  
Tobias Wolf ◽  
Lasse H. Pettersson ◽  
Igor Esau
Keyword(s):  
Air Pollution ◽  
Spatial Distribution ◽  
Air Quality ◽  
Complex Terrain ◽  
Diffusion Problem ◽  
Wood Combustion ◽  
Urban Districts ◽  
Advection Diffusion ◽  
Large Eddy

Abstract. Many cities in the world experience significant air pollution from residential wood combustion. Such an advection-diffusion problem as applied to geographically distributed small-scale pollution sources presently does not have a satisfactory theoretical or modelling solution. For example, statistical models do not allow for pollution accumulation in local stagnation zones – a type of phenomena that is commonly observed over complex terrain. This study applies a Parallelized Atmospheric Large-eddy simulation Model (PALM) to investigate dynamical phenomena that control variability and pathways of the atmospheric pollution emitted by wood-burning household stoves. The model PALM runs with a real spatial distribution of the pollution source and with realistic surface boundary conditions that characterize a medium-sized urban area fragmented by water bodies and hills. Such complex geography is expected to favor local air quality hazards, which makes this study of general interest. The case study here is based on winter conditions in Bergen, Norway. We investigate the turbulent diffusion of a passive scalar associated with small sized particles (PM2.5) emitted by household stoves. The study considers air pollution effects that could be observed under different policy scenarios of stove replacement; modern wood stoves emit significantly less PM2.5 than the older ones, but replacement of stoves is costly and challenging process. To solve the advection-diffusion problem, we used the PALM model at spatial resolution of 10 m in an urban-sized modelling domain of 15 km by 15 km. We found significant accumulation of near-surface pollution in the local stagnation zones. The observed concentrations were larger than concentrations due to the local PM2.5 emission, thus, indicating dominant trans-boundary contribution of pollutants for other districts. We demonstrate how the source of critical pollution can be attributed through model disaggregation of emissions from specific districts. The study reveals a decisive role of local air circulations over complex terrain that makes high-resolution modelling indispensable for adequate management of the urban air quality.This modelling study has important policy-related implications. Uneven spatial distribution of the pollutants suggests prioritizing certain limited urban districts in policy scenarios. We show that focused efforts towards stove replacement in specific areas may have dominant positive effect on the air quality in the whole municipality. The case study identifies urban districts where limited incentives would result in the strongest reduction of the population’s exposure to PM2.5.

scholarly journals From Black to Blue Skies: Civil Society Perceptions of Air Pollution in Shanghai

2021 ◽  
pp. 1-22
Author(s):  
Amanda K. Winter ◽  
Huong Le ◽  
Simon Roberts
Keyword(s):  
Air Pollution ◽  
Civil Society ◽  
Air Quality ◽  
Qualitative Case Study ◽  
Field Research ◽  
Ecological Civilization ◽  
Civil Society Participation ◽  
Quality Indexes

Abstract This paper explores the perception and politics of air pollution in Shanghai. We present a qualitative case study based on a literature review of relevant policies and research on civil society and air pollution, in dialogue with air quality indexes and field research data. We engage with the concept of China's authoritarian environmentalism and the political context of ecological civilization. We find that discussions about air pollution are often placed in a frame that is both locally temporal (environment) and internationally developmentalist (economy). We raise questions from an example of three applications with different presentations of air quality index measures for the same time and place. This example and frame highlight the central role and connection between technology, data and evidence, and pollution visibility in the case of the perception of air pollution. Our findings then point to two gaps in authoritarian environmentalism research, revealing a need to better understand (1) the role of technology within this governance context, and (2) the tensions created from this non-participatory approach with ecological civilization, which calls for civil society participation.

scholarly journals Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

2017 ◽  
Vol 17 (11) ◽  
pp. 7261-7276 ◽  
Author(s):  
Tobias Wolf-Grosse ◽  
Igor Esau ◽  
Joachim Reuder
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Large Eddy Simulation ◽  
Air Pollutants ◽  
Large Scale ◽  
Wind Speeds ◽  
Street Level ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Air Pockets

Abstract. Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s−1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

scholarly journals IMPROVING THE QUALITY OF INFORMATION SUPPORT FOR MONITORING AIR POLLUTION FROM VEHICLES (CASE STUDY OF ST. PETERSBURG)

2021 ◽  
Vol 26 (2) ◽  
pp. 65-74
Author(s):  
V. N. Lozhkin ◽  
◽  
O. V. Lozhkina ◽  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Information Support ◽  
Information Process ◽  
Limit Values ◽  
Standard Limit ◽  
The City ◽  
Original Information

Introduction. St. Petersburg is the cultural and sea capital of Russia. The city is characterized by environmental problems typical for the largest cities in the world. It has a technical system for instrumental online monitoring and computational forecasting of air quality. Methods. The system maintains the information process by means of computational monitoring of its current and future state. Results. The paper describes methodological approaches to the generation of instrumental information about the structure and intensity of traffic flows in the urban road network and its digital transformation into GIS maps of air pollution in terms of pollutants standard limit values excess. Conclusion. The original information technology for air quality control was introduced at the regional level in the form of an official methodology and is used in environmental management activities.

scholarly journals Case Study on Anthropogenic Air Pollution in Nagpur City

2021 ◽  
Vol 9 (VI) ◽  
pp. 2279-2282
Author(s):  
Mukul Dayaramani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Harmful Effect ◽  
Central India ◽  
Well Being ◽  
Good Health ◽  
Air Pollutant ◽  
Corporate Community ◽  
Health And Well Being

Air pollution is a very serious problem worldwide. Anthropogenic air pollution is mostly related to the combustion of various types of fuels. Air pollutant levels remain too high and air quality problems are still not solved. The presence of pollutants in the air has a harmful effect on the human health and the environment. Good air quality is a prerequisite for our good health and well-being. Nagpur city is located in Maharashtra state of central India. Business hub and increased industrialization in study area is affecting the environment adversely. n. Changing life style of corporate community and their effects on other population enhancing the contamination of environment

scholarly journals Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

2019 ◽  
Vol 147 (12) ◽  
pp. 4325-4343 ◽  
Author(s):  
Cornelius Hald ◽  
Matthias Zeeman ◽  
Patrick Laux ◽  
Matthias Mauder ◽  
Harald Kunstmann
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Computing Time ◽  
Remote Sensing Data ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Sensing Data ◽  
Large Eddy ◽  
Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

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 The Use of the Internet of Things for Estimating Personal Pollution Exposure

2019 ◽  
Vol 16 (17) ◽  
pp. 3130 ◽  
Author(s):  
Keith April G. Arano ◽  
Shengjing Sun ◽  
Joaquin Ordieres-Mere ◽  
and Bing Gong
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Internet Of Things ◽  
Policy Development ◽  
Prediction Models ◽  
Low Cost ◽  
Activity Patterns ◽  
Dynamic Feedback ◽  
Pollution Exposure

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.

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