Street-level heat and air pollution exposure informed by mobile sensing

Batur, İrfan; Markolf, Samuel A.; Chester, Mikhail; Middel, Ariane; Hondula, David M.; Vanos, Jennifer

doi:10.1016/j.trd.2022.103535

Cited by 9 publications

(6 citation statements)

References 53 publications

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“…As we increased the dataset by combining three AQM stations (Ratnapark, Shankapark, and Pulchowk) data in dataset 2, the result of Table 5 shows that the model performance improved after including the metrological data. The average change in R 2 and RMSE values across all algorithms is +0.032 and -0.77, respectively, indicating an overall improvement in model performance on dataset 2 compared to dataset 1. It was seen that the size of the dataset had a great impact on the model performance.…”

Section: Resultsmentioning

confidence: 99%

“…One of the most common and accurate methods for monitoring air quality is through air quality monitoring stations. However, measurements are only available in the surrounding area of the stations [2]. Through air quality monitoring, air pollutant concentration data are obtained to determine whether the concentration levels are good, unhealthy for sensitive groups, or at emergency levels.…”

Section: Introductionmentioning

confidence: 99%

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Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Thapa,

Devkota

2024

J. Eng. Sci.

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Air pollution, particularly fine Particulate Matter (PM 2.5), poses significant health risks and environmental challenges worldwide. Therefore, it is essential to monitor air pollution to act on it. In this study, PM 2.5 was estimated using meteorological data and Sentinel-5P air pollution data using machine learning algorithms. The Sentinel-5P data are and the meteorological data utilized are air temperature, Relative Humidity (RH), and Wind Speed (WS). The three Air Quality Monitoring (AQM) stations in Kathmandu, Nepal, were chosen as a study area for this research. The effectiveness of several machine learning methods, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), were evaluated. Both RF and XGBoost consistently performed better than SVM and KNN in terms of PM 2.5 estimation accuracy. RF got the highest R2 value of 0.80 and SVM with the lowest R2 value of 0.62 in the Sentinel-5P dataset only. The addition of meteorological data further improved the model's performance. After including metrological data in Sentinel-5P data the RF demonstrated the maximum R2 score of 0.816 and XGBoost with R2 score of 0.814. Hence, this study demonstrated machine learning algorithms can be used to estimate PM 2.5 by utilizing satellite and meteorological data, providing important information for air quality monitoring and management.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Thapa,

Devkota

2024

J. Eng. Sci.

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“…Vehicle‐borne mobile sensing can be broadly categorized into three main types based on the monitored objects: natural environment monitoring, built environment monitoring, and human activity monitoring. The monitoring of the natural environment through the use of sensors mounted on vehicles primarily involves ambient air temperature (Yin et al., 2020), particulate matter (air quality) (Batur et al., 2022), noise levels (Patil, 2017), vehicle energy consumption and emissions (Sun et al., 2015), gas concentrations (Tao et al., 2015), weather conditions (Mahoney et al., 2010), and other related variables. In the realm of monitoring the built environment, vehicle‐borne sensors have been employed to assess the deterioration of railroad bridges (Allard, 2017), as well as to examine the condition of road and railway infrastructure through the utilization of laser scanning technologies (Soilán et al., 2019).…”

Section: Related Workmentioning

confidence: 99%

Revealing the mobile sensing powers of semi‐random and deterministic “drive‐by” vehicle fleets

Jiang,

Wang,

Yao

et al. 2023

Transactions in GIS

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The utilization of mobile sensing through the deployment of sensors on third‐party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost‐effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi‐random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure.

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“…In recent years, mobile air quality sensors have been widely used in the field of atmospheric environmental monitoring and pollution traceability since they are a measurement tool with low costs, high mobility and flexibility, and various application scenarios [5][6][7][8][9][10][11][12][13]. Compared with the fixed monitoring method, which has some shortcomings, such as blind spots in monitoring and the inability to accurately locate pollution sources, the use of a mobile air quality sensor improves the accuracy and coverage of environmental monitoring.…”

Section: Instrumentmentioning

confidence: 99%

Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China

Cai

Yang

et al. 2022

Sustainability

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Mobile observation improves the accuracy and coverage of environmental monitoring, and can locate and track pollution sources. We conducted mobile monitoring to obtain real-time atmospheric pollutants (PM2.5, PM10, SO2, NO2, CO and O3) in typical areas, which included a country park and a tourist attraction featuring an ancient town in the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone (Demonstration Zone), China. Results show that the concentrations of the six key pollutants in the ancient town were usually higher than that in the country park, due to high intensity of anthropogenic emissions. Pollutants including PM2.5, PM10, SO2 and CO in the ancient town during weekends were higher than that during weekdays, whereas pollutants in the country park presented no difference during weekdays and weekends. Morphology analysis of individual particles by scanning electron microscopy detected abundant soot from fresh emissions and atmospheric aging in the two areas. Agricultural irrigation, powered by diesel combustion, was identified as an emission source in the country park. Open-air cooking, coal combustion for cooking and the frequent redecoration of stores were emission sources in the ancient town. Environmentally friendly agricultural irrigation ways and cleaner cooking fuels were suggested to further improve air quality in the Demonstration Zone.

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Street-level heat and air pollution exposure informed by mobile sensing

Cited by 9 publications

References 53 publications

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Revealing the mobile sensing powers of semi‐random and deterministic “drive‐by” vehicle fleets

Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China

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