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Boxplot demonstrating distribution of minutes of sampling per 100 m segment for each sampling route.
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Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR...
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Context 1
... (46.6 ± 28.3 μg/m 3 ) and night (54.8 ± 24.1 μg/m 3 ). The amount of sampling data varied across the 215 segments, with a median of 44 min per segment (minimum = 5; 25% percentile = 34; 75% percentile = 55; maximum = 179). Summary statistics for minutes of sampling per 100 m segment for each of the five routes are visualized as boxplots in Fig. ...
Citations
... In 2013, PM 2.5 was declared a Group 1 carcinogen by the World Health Organization (WHO). In recent years, studies related to the "risk to human health from PM 2.5 " have increased significantly, attracting the attention of researchers globally (Lim et al. 2019;Chen et al. 2018;Choudhuri and Singh 2022). Besides its health effects, it is a major threat to climatic change, socio-economics, and biodiversity. ...
Fine particulate matter (PM 2.5) has become a prominent pollutant due to rapid economic development, urbanization, indus-trialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM 2.5 concentrations. However, statistical models have shown inconsistency in PM 2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM 2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NO X , SO 2 , CO, and O 3) on the dynamics of PM 2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM 2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO 2 , NO X , and O 3. Precipitation, relative humidity, and temperature have negative correlations with PM 2.5. The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM 2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM 2.5 concentrations. This study will help quantify ground-level PM 2.5 concentration exposure and recommend regional government actions to prevent and regulate PM 2.5 air pollution.
... Previous studies [1,4,[20][21][22] have suggested numerous methods for mapping PM2.5 in Ulaanbaatar. The scale of the resulting dispersion maps, however, is too great, and some of the maps merely show the city's core, without expanding outward. ...
... The ger area's fine particulate matter pollution is around two times higher than the city center's fine particulate matter pollution [25]. Based on other researchers' experience [20][21][22]26], the "DUST TRAK II Aerosol Monitor 8532" mobile device was borrowed from the Department of Environment and Forest Engineering, National University of Mongolia, and was utilized for the field study. This portable device has a "Certificate of Calibration and Testing" (certificate serial number: 8532134301) and can measure aerosol concentrations for PM1, PM2.5, respirable, or PM10 size fractions with a corresponding impactor kit. ...
... Fixed stations and mobile device measurements' descriptive statistics are shown in Table 1. Based on other researchers' experience [20][21][22]26], the "DUST TRAK II Aerosol Monitor 8532" mobile device was borrowed from the Department of Environment and Forest Engineering, National University of Mongolia, and was utilized for the field study. This portable device has a "Certificate of Calibration and Testing" (certificate serial number: 8532134301) and can measure aerosol concentrations for PM 1 , PM 2.5 , respirable, or PM 10 size fractions with a corresponding impactor kit. ...
In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city
center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city’s residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM)
and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83,
RMSE=53.25µg/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44µg/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, MSE=55.6 µg/m3, MAE=38.7 µg/m3, and GAM LOOCV R2 = 0.77, RMSE=65.5 µg/m3, MAE=47.7 µg/m3. From these results, the LUR model’s performance is high, especially the GAM model, which works better than MRLM.
... The involvement of citizen scientists is beneficial to allowing to gather additional data and to significantly increase the number of measurements with relatively low effort. In recent years, several citizen science projects have been carried out on air pollution in general, e.g., in urban areas in California and Colorado in the United States, in the Republic of Korea, and in Kenya [11][12][13][14], and specifically on urban NO2 concentrations, e.g., in Italy [15]. An impressive example of a large citizen science project on air pollution is a NO2 distribution survey over Flanders (Belgium) in 2018. ...
Nitrogen dioxide (NO2) is a major air pollutant with diverse impacts on human health and the environment. In urban areas, road traffic is the main emission source for NO2. In Berlin, Germany, a network of measurement stations is operated by the state, fulfilling the monitoring requirements set by the European Union. To get a more detailed overview of the spatial distribution of NO2 concentrations in Berlin, a citizen science project allowed for collection of additional data and an increase in the number of sampling sites. Passive samplers (modified Palmes tubes) were distributed to participants to collect NO2 at a site of their choice. When returned, the samplers were analyzed based on the Griess–Ilosvay reaction and spectrophotometric detection. The results confirmed a seasonal trend of higher NO2 concentrations in winter and lower concentrations during the summer period. Furthermore, the spatially and monthly averaged NO2 concentrations observed in the study period from March 2019 to October 2020 were in good agreement with the average urban background concentration. At small spatial scales, a tendency of decreasing NO2 concentrations with increasing distance from roads was observed. Overall, this study shows the added benefit of extensive low-cost measurements of NO2 concentrations across urban environments in a citizen science project to complement stationary air pollution monitoring networks.
... For conducting the relationship between air pollution, human activities, and health at the urban scale in China, previous studies have shown that the fixed air pollution monitoring stations are spatially uneven with high construction operation and maintenance costs (Li et al., 2021;Zhang et al., 2019a), only represent air pollution in a small surrounding area and cannot meet the requirement of high spatial-temporal resolution studies to reflect the real exposure of residents (Dias and Tchepel, 2018). With the development of production and technology, low-cost mobile monitoring devices have been proven to improve the spatial-temporal resolution of air pollution (Lim et al., 2019). Meanwhile, the study indicates that there are spatial differences in the distribution of air pollution on weekdays and weekends (Requia et al., 2018;Wong et al., 2009). ...
... Exposure to PM may cause acute or chronic effects on the human body and cause adverse effects on economic development (Ma et al., 2011). PM monitoring based on high spatial and temporal resolution is helpful to understand the complex changes caused by the complex landscape structure in cities (Lim et al., 2019). This study indicated that as the PM diameter decreases, model accuracy increases on weekdays and decreases on weekends. ...
... Studies employing Big Data analytics and machine learning methods for air quality prediction and management are also abound. For instance, random forest and stacked ensemble have been used in a study focused on mapping urban air quality using mobile sampling in Seoul, South Korea (Lim et al., 2019). Some sectors such as transport have received more attention as can be seen from Fig. 5.1. ...
Efforts aimed at addressing climate change have gained significant momentum in the past few years and following the Paris Climate Agreement. Recent data show that global temperatures are already about 1.1°C above pre-industrial levels and the window to limit global warming to 1.5°C or 2°C is rapidly closing. Cities account for over 70% of global CO2 emissions, indicating their significance for achieving climate stabilization targets. Recognizing this, many cities around the world are increasingly developing plans and strategies to contribute to climate change mitigation. In the meantime, smart technologies are rapidly becoming ubiquitous in many cities around the world and governments and local authorities have used this as an opportunity to develop and implement smart city programs. While the impacts of smart city programs on urban CO2 emissions are not yet fully examined, it is hoped that they can contribute to meeting climate change mitigation targets. Through text mining of bibliometric data archived in the Web of Science, this chapter seeks to provide an overview of existing research at the intersection of climate change mitigation and smart city solutions and technologies. The chapter aims to discuss actual and potential contributions of smart city solutions, related to various urban systems, to climate change mitigation. Based on outputs of bibliometric analysis (term co-occurrence) obtained from the VOSviewer software tool, issues related to urban planning, buildings, transportation, waste management, energy and water resource, economy, urban infrastructure, and urban governance are discussed. It is argued that smart solutions and technologies have high potential to contribute to climate change mitigation. They can also provide co-benefits for climate change adaptation and sustainable development. However, appropriate planning and regulating measures are needed to avoid potential trade-offs and rebound effects. Such effects and trade-offs have also been discussed. Further, the need for integrated systems that accommodate different urban sectors is highlighted.
... Studies employing Big Data analytics and machine learning methods for air quality prediction and management are also abound. For instance, random forest and stacked ensemble have been used in a study focused on mapping urban air quality using mobile sampling in Seoul, South Korea (Lim et al., 2019). Some sectors such as transport have received more attention as can be seen from Fig. 5.1. ...
Urban Climate Adaptation and Mitigation offers evidence-based, scientific solutions for improving a city's ability to prepare, recover and adapt to global climate-related events. Bringing together a wide variety of research disciplines to addresses the linkages to climate change adaptation and mitigation topics with planning, transportation and waste management, the book informs different types of stakeholders on how they can enhance their preparation abilities to enable real-time response methods. Application-focused throughout, this book explores the complexities of urban systems and subsystems to support researchers, planners and decision-makers in their efforts toward developing more climate-resilient smart cities.
... Regardless of a strictly laboratory calibration approach or a combined field and laboratory calibration approach, linear regression is most commonly used to create the calibration model despite known non-linear relationships between PM 2.5 and meteorological variables [13,15]. While non-linear models have been developed for sensor calibration, these models still only produce point predictions without estimates of variance [17][18][19][20]. However, given that EPA and NIOSH both recommend probabilistic exposure and risk assessments, more accurate assessments are possible if point and/or uniform variance predictions are replaced with predictions from models that also describe variance, particularly on a per-prediction level [21][22][23]. ...
... Large-scale approaches often utilize satellite data, country scale sensor networks, land use data, topography, etc. and have been built using random forests, GBDTs, and neural nets [31][32][33][34]. On a smaller scale more analogous to SEARCH, personal monitoring device networks, mobile sampling networks, and cityscale sensor networks have also demonstrated the utility of machine learning regression techniques to optimize predictions and take into account environmental factors [17][18][19][20]. However, while prediction of PM 2.5 using sensor measurements and additional data has been conducted by numerous studies, this study fills a unique position by providing a methodology for both increasing the utility of low-cost sensor networks by creating a probabilistic output useful for exposure assessments, a state-ofthe-art model that improves on existing approaches, and also removes the need for lab-calibrated data, a time intensive process for mitigating environmental biases for PM 2.5 data. ...
Background
Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment.
Objective
Build direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level.
Methods
Using data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore.
Results
We demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 μg/m³, and also on monitors not included in the training set.
Significance
We provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD.
Impact statement
We demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process.
Graphical abstract
... Previous studies have reported the implementation of smart sensors in a mobile air pollution framework. A vehicular wireless sensor network architecture was implemented at the National Chiao-Tung University in Taiwan (Hu et al. 2009), and researchers in Seoul, South Korea, mapped urban air quality using mobile sampling with low-cost sensors and machine learning (Lim et al. 2019). The public buses in Sharjah city, United Arab Emirates, were also used to test an air pollution sensing network (Al-Ali et al. 2010) while in New Jersey and New York, the United States, a finegrained vehicular-based mobile air pollution measuring technique using solid-state carbon monoxide (CO) sensors and optical analysers (PM) was used to measure 'on road' pollution (Devarakonda et al. 2013). ...
Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas.
... Without optimal sensing granularity that results in considerable spatial data gaps, it is impossible to accurately map the urban air quality-a challenge that can be overcome by combining the crowdsourced measurements with model data with comprehensive spatial coverage . Other deployment methods include IoT sensor installation on mobile sensing platforms such as bicycles, cars, buses, and trams that can improve spatial coverage (Devarakonda et al., 2013;Mead et al., 2013;Castell et al., 2015;Hasenfratz et al., 2015;Lim et al., 2019). While economically more appealing due to the significantly reduced number of sensors, the mobility of the platform in conjunction with a prolonged response time of typical sensors can cause large signal distortion-an issue that can be overcome through the application of active sampling that employs pumps/fans as actuators (Arfire et al., 2016). ...
Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.
... Fourth, the effect of background concentrations was minimized according to the methods of a previous study (Lim et al., 2019;Tessum et al., 2018). Because the meteorological data vary considerably from day to day during the actual measurements, the PM 2.5 concentrations depend on the background concentrations on that day. ...
... Because the meteorological data vary considerably from day to day during the actual measurements, the PM 2.5 concentrations depend on the background concentrations on that day. The previous moving sample survey adjusted for potential temporal bias by subtracting the daily fifth percentile (Tessum et al., 2018) or used the ratio of the average concentration level at nearby monitors during the sampling period to the corresponding hourly monitored concentration (Lim et al., 2019). We adjusted the daytime temporal trend by subtracting the background point concentration value at the corresponding time from all the concentration values obtained from mobile monitoring: ...
Concentrations of airborne particulate matter (PM) are influenced by land cover types. Water bodies in cities can influence the spatial distribution of air pollution by altering the microclimate. However, the influence of water bodies on PM2.5 concentrations is complex and requires further exploration, especially at the microscale. In this study, a geographically weighted regression (GWR) model was constructed to explore the spatially heterogeneous effect of a river on the PM2.5 concentrations in nearby riverside neighborhoods using mobile monitoring in Wuhan. The results showed that the GWR model was applicable at the neighborhood scale and had a good explanatory performance for PM2.5 concentrations, with a higher R2 (0.71). The river had multiple effects on the PM2.5 concentrations in the riverfront neighborhoods and strongly affected the air quality in neighborhoods within an 800 m distance due to wind infiltration, with the strongest effect at a 500–700 m distance. Moreover, considering spatial nonstationarity, the effect of the large river on street-level air quality largely depended on its effect on wind, and good ventilation conditions could amplify that effect. Commercial, road intersections, second-level roads and parks were identified as sensitive environmental factors affecting the river’s influence on PM2.5 concentrations. In addition, urban parks had a greater mitigating effect on PM2.5 pollution than did water bodies in this study. These results help to clarify the impact of rivers on air quality and provide a theoretical basis for urban design to mitigate PM2.5 pollution.
