Emission estimation based on traffic models and measurements

Tsanakas, Nikolaos

doi:10.3384/lic.diva-155771

Cited by 4 publications

(4 citation statements)

References 77 publications

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“…The pollution in Foshan was mainly in Nanhai District, while the PM 2.5 mass concentrations in Dongguan were mainly distributed in the western, central, and southeastern regions. In addition, ultrahigh resolution retrieval results were also used to analyze the pollution characteristics of different cities on the day; the industrial pollution was dominant in Guangzhou and Foshan, while traffic pollution was the main factor in Dongguan which was consistent with other studies on pollution characteristics of industrial cities [61,62]. Finally, the retrieval results were verified by using MODO4 DB AOD products and ground monitoring station data.…”

Section: Discussionsupporting

confidence: 71%

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

Tang

Deng

et al. 2021

Remote Sensing

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The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

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Section: Discussionsupporting

confidence: 71%

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

Tang

Deng

et al. 2021

Remote Sensing

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“…The geometry of the road network was connected with descriptive data, including information on the names of streets, types The first stage of the studies was determining the average annual daily traffic volumes (AADT) in the road transport (n/h) in the given road segment of the analyzing road [34]. AADT was the basic parameter calculated for all segments of the road network, while the method of its calculation depends on the type of measurement segment [35] and [36] (pp. 80-85).…”

Section: Research Methodology and Datamentioning

confidence: 99%

Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Kwiecień

Szopińska

2020

Remote Sensing

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Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.

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“…The magnitude of the combined effects on health and environment determine the economic effects of air pollution (Eliasson, 2009;Smit et al, 2010). An extended description of the main air pollutants and their effects on human health is given by Tsanakas (2019).…”

Section: Emissions From Road Trafficmentioning

confidence: 99%