Adjusted R 2 of LR LUR models (including all available 67 predictor variables) for mass, by segment radius and buffer sizes.

Adjusted R 2 of LR LUR models (including all available 67 predictor variables) for mass, by segment radius and buffer sizes.

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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
... LUR models were sensitive to different segment lengths and buffer radiuses, with adj-R 2 generally increasing with larger buffer radiuses (Fig. 5), while 100 m to 150 m segments for spatial aggregation performed the best. Fitting individual equations to account for intrainstrument variability for each AirBeam unit generally improved the accuracy of the constructed LUR models, with an increase in CV-R 2 values by ...

Citations

... 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. ...
Chapter
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. ...
Book
Full-text available
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. ...
Article
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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). ...
Article
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). ...
Article
Full-text available
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: ...
Article
Full-text available
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.
... Land-use regression can provide air quality estimations with a high spatial resolution but lack sufficient temporal resolution. Meanwhile, it highly relies on the availability of updated local land-use data [14][15][16]. Some studies use satellite remote sensing data to estimate air quality [17,18], but they are usually spatially coarse (1−10 km resolution) [19] and easily affected by cloudy weather and water/snow glint reflectance [20,21]. ...
... Third, the LCS sensors have higher spatial-temporal measurement resolution [5,30], but existing LCS-based methods still have some limitations. The portable monitoring devices are not necessarily accurate due to cost and volume limitations, and often focus on a specific area rather than the whole city [14]. The vehicles equipped with sensors cannot guarantee the monitoring time (less observation at night) and are easily affected by human factors (forgetting to open) or operating environments (the wind in driving) [19,31]. ...
... The high-resolution air quality inferences may be useful for multicenter health studies with highly dense urban populations. A variety of methods have been used for urban air quality inference, but still lack sufficient spatio-temporal coverage, resolution, and sustainability to meet social needs [5,14,19,45]. The lack of sufficient monitoring data and stable monitoring equipment may be the reason for this progress being hindered. ...
Article
Full-text available
Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.
... Moreover, they can be sensitive towards environmental conditions and other pollutants and experience sensor drift over time [25][26][27][28][29]. In the literature, a wealth of studies is available on the lab-or field-based evaluation of sensors [30][31][32][33][34][35][36][37][38][39][40][41], co-location or network calibration approaches [25,26,28,[42][43][44][45][46][47][48][49], personal exposure [3,[50][51][52] and mapping [53][54][55][56][57][58] proof of concept applications. These studies often conclude that air quality sensors have the potential for policy support, but fall short in addressing real-life policy concerns or in indicating how such a monitoring campaign could be set up methodologically or controlled in terms of data quality. ...
Article
Full-text available
(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.
... As deducted from the first campaign (Section 3.1), only morning and evening peak hours were investigated and only two settings (windows-open; recirculation and AC on) for car were studied. A pDR-1500 personal air quality monitor was used for this campaign as a reliable monitor used in earlier global studies (Koehler and Peters, 2015;Lim et al., 2019;Pant et al., 2017;Zhang et al., 2017). The monitor was factory calibrated and an additional quality control and assurance exercise was carried out to further validate pDR-1500 measurements (Section 5.2.3). ...
... pDR-1500 has been widely used in prior personal monitoring studies (e.g. Koehler and Peters, 2015;Lim et al., 2019;Pant et al., 2017;Zhang et al., 2017). The same private car (a 2011 ...
Thesis
Air pollution causes 65,000 premature deaths across the Middle East and North Africa every year, where 99% of the population is exposed to pollution that exceeds World Health Organization standards. Nevertheless, studies on the issue are limited. Greater Cairo is the largest megacity in the region where commuters are exposed to excessive pollution. Based on reviewing the literature, conducting field campaigns and emission modelling, this thesis focuses on Greater Cairo on-road transport with the aim to (i) understand car users’ exposure to particulate matter (PM10 with aerodynamic diameter ≤10 µm and PM2.5 ≤2.5 µm) and gaseous pollutants under different car settings, (ii) compare PM2.5 exposure in car, microbus, cycling and walking commutes, (iii) assess five transport emission control scenarios against a ‘do nothing’ scenario for 2030, and finally (iv) estimate the corresponding health burden and economic losses associated with exposure to PM2.5 during commutes. This thesis includes two field campaigns where personal exposure air quality data were collected using portable monitors during daily commutes. Emission modelling was also carried out to evaluate mitigation measures. Collected data were analysed to understand concentration variations, spatial variability and hot spots, PM2.5/PM10 ratios, exposure doses, commuting costs, assess control scenarios and estimate health burden and economic losses. Several significant findings were noted from the analysis: (i) the choice of car setting affects exposure levels where windows-open resulted in 48% higher PM2.5 concentrations compared to recirculation and AC, (ii) pedestrians and cyclists were exposed to 3.1-times PM2.5 levels of car users with recirculation and AC, (iii) in terms of emission control measures, inspection and maintenance programs proved most effective in reducing health-damaging pollutants while public transport reduced overall emissions and improved the quality of life, (iv) deaths in the microbus population contributed to 57% of the economic losses due to PM2.5 amongst the four modes.