Spanddhana Sara, A. Rebeiro-Hargrave, Shreyash Gujar, O. Kathalkar, Samu Varjonen, Sachin Chaudhari, S. Tarkoma
Particulate Matter (PM) is a major air pollutant that has the potential for adversely affecting human health. Actionable data on the spatial distribution of temporal variability of PM2.5 emission hot spots in large cities are sparse. The main objective of this research is to provide a protocol for using search agents to hunt for PM2.5 emission hot spots in urban environments. We propose short range identification of variability of harmful PM2.5 concentrations can be achieved using IoT devices mounted on a mobile platform. We propose that long range identification the PM2.5 emission hot spots can attained by searching through the city on different days. We applied this approach to Hyderabad, India by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices. The spatial variability of PM2.5 harmful emission hot spots at industrial settings and congested roads were identified. The temporal variability based on image processing shows a weak correlation between PM2.5 concentrations and number of vehicles, and PM2.5 and visibility. The Hyderabad PM2.5 emission hot spots findings demonstrate a clear need to inform people with heart and lung conditions when it is unhealthy to be outside; and when it is unhealthy for children and elderly people to be outside for prolonged periods. Our emission hunting approach can be applied to any mobile platform carried by people walking, cycling or by drones and robots in any city.
{"title":"Protocol for hunting PM2.5 emission hot spots in cities","authors":"Spanddhana Sara, A. Rebeiro-Hargrave, Shreyash Gujar, O. Kathalkar, Samu Varjonen, Sachin Chaudhari, S. Tarkoma","doi":"10.1145/3597064.3597322","DOIUrl":"https://doi.org/10.1145/3597064.3597322","url":null,"abstract":"Particulate Matter (PM) is a major air pollutant that has the potential for adversely affecting human health. Actionable data on the spatial distribution of temporal variability of PM2.5 emission hot spots in large cities are sparse. The main objective of this research is to provide a protocol for using search agents to hunt for PM2.5 emission hot spots in urban environments. We propose short range identification of variability of harmful PM2.5 concentrations can be achieved using IoT devices mounted on a mobile platform. We propose that long range identification the PM2.5 emission hot spots can attained by searching through the city on different days. We applied this approach to Hyderabad, India by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices. The spatial variability of PM2.5 harmful emission hot spots at industrial settings and congested roads were identified. The temporal variability based on image processing shows a weak correlation between PM2.5 concentrations and number of vehicles, and PM2.5 and visibility. The Hyderabad PM2.5 emission hot spots findings demonstrate a clear need to inform people with heart and lung conditions when it is unhealthy to be outside; and when it is unhealthy for children and elderly people to be outside for prolonged periods. Our emission hunting approach can be applied to any mobile platform carried by people walking, cycling or by drones and robots in any city.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naser Hossein Motlagh, M. A. Zaidan, P. Fung, A. Rebeiro-Hargrave, M. Irjala, T. Hussein, T. Petaja, P. Nurmi, S. Tarkoma
The rapid growth of megacities has led to higher levels of air pollution in cities. To supplement fixed air quality monitoring sites, the megacities offer an unprecedented opportunity to deploy air quality sensors on public transportation systems, and thus enable air quality monitoring at different locations in the city within the routes of the transport means. In this paper, we leverage this opportunity and show the feasibility of deploying air quality sensors on the means of the transport system by installing three low-cost sensors on three trams that operate on three different routes in the city of Helsinki. Our measurement campaign took place during the summer and autumn of 2019, during which we measured main traffic pollutants and meteorological variables. Specifically, we show the variability of pollution levels in different locations using pollution hotspots captured by one of the sensors for two of the main air pollutants. That is, we demonstrate the potential of deploying the sensors on public transport systems and show the feasibility and effectiveness of such an approach for pollution hotspot detection; this can enable real-time air quality information streaming, and thus contribute to the cities' air quality repositories used for the public.
{"title":"Feasibility of Air Quality Monitoring from Transport Vehicles","authors":"Naser Hossein Motlagh, M. A. Zaidan, P. Fung, A. Rebeiro-Hargrave, M. Irjala, T. Hussein, T. Petaja, P. Nurmi, S. Tarkoma","doi":"10.1145/3597064.3597363","DOIUrl":"https://doi.org/10.1145/3597064.3597363","url":null,"abstract":"The rapid growth of megacities has led to higher levels of air pollution in cities. To supplement fixed air quality monitoring sites, the megacities offer an unprecedented opportunity to deploy air quality sensors on public transportation systems, and thus enable air quality monitoring at different locations in the city within the routes of the transport means. In this paper, we leverage this opportunity and show the feasibility of deploying air quality sensors on the means of the transport system by installing three low-cost sensors on three trams that operate on three different routes in the city of Helsinki. Our measurement campaign took place during the summer and autumn of 2019, during which we measured main traffic pollutants and meteorological variables. Specifically, we show the variability of pollution levels in different locations using pollution hotspots captured by one of the sensors for two of the main air pollutants. That is, we demonstrate the potential of deploying the sensors on public transport systems and show the feasibility and effectiveness of such an approach for pollution hotspot detection; this can enable real-time air quality information streaming, and thus contribute to the cities' air quality repositories used for the public.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131671702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J.A. Paredes-Ahumada, Pau Ferrer-Cid, J. Barceló-Ordinas, J. García-Vidal, C. Reche, M. Viana
Air quality monitoring sensor networks focusing on air pollution measure pollutants that are regulated by the authorities, such as CO, NO2, NO, SO2, O3, and particulate matter (PM10, PM2.5). However, there are other pollutants, such as black carbon (BC), which are not regulated, have a major impact on health, and are rarely measured. One solution is to use proxies, which consist of creating a mathematical model that infers the measurement of the pollutant from indirect measurements of other pollutants. In this paper, we propose a robust machine learning proxy (RMLP) framework for estimating BC based on nonlinear machine learning methods, calibrating the low-cost sensors (LCSs), and adding robustness against noise and data missing in the LCS. We show the impact of LCS data aggregation, denoising and missing imputation on BC estimation, and how the concentrations estimated by the BC proxy approximate the values obtained by a reference instrument with an accurate BC sensor.
专注于空气污染的空气质量监测传感器网络测量受当局管制的污染物，如CO, NO2, NO, SO2, O3和颗粒物(PM10, PM2.5)。然而，还有其他污染物，如黑碳(BC)，不受管制，对健康有重大影响，很少进行测量。一种解决方案是使用代理，它包括创建一个数学模型，通过对其他污染物的间接测量来推断污染物的测量。在本文中，我们提出了一个鲁棒的机器学习代理(RMLP)框架，用于基于非线性机器学习方法估计BC，校准低成本传感器(LCS)，并增加对LCS中噪声和数据缺失的鲁棒性。我们展示了LCS数据聚合、去噪和缺失代入对BC估计的影响，以及BC代理估计的浓度如何接近具有精确BC传感器的参考仪器获得的值。
{"title":"Robust Proxy Sensor Model for Estimating Black Carbon Concentrations Using Low-Cost Sensors","authors":"J.A. Paredes-Ahumada, Pau Ferrer-Cid, J. Barceló-Ordinas, J. García-Vidal, C. Reche, M. Viana","doi":"10.1145/3597064.3597316","DOIUrl":"https://doi.org/10.1145/3597064.3597316","url":null,"abstract":"Air quality monitoring sensor networks focusing on air pollution measure pollutants that are regulated by the authorities, such as CO, NO2, NO, SO2, O3, and particulate matter (PM10, PM2.5). However, there are other pollutants, such as black carbon (BC), which are not regulated, have a major impact on health, and are rarely measured. One solution is to use proxies, which consist of creating a mathematical model that infers the measurement of the pollutant from indirect measurements of other pollutants. In this paper, we propose a robust machine learning proxy (RMLP) framework for estimating BC based on nonlinear machine learning methods, calibrating the low-cost sensors (LCSs), and adding robustness against noise and data missing in the LCS. We show the impact of LCS data aggregation, denoising and missing imputation on BC estimation, and how the concentrations estimated by the BC proxy approximate the values obtained by a reference instrument with an accurate BC sensor.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Langhorst, S. Orzan, Teade Punter, Bernd-Jan Witkamp
As many cities do, Eindhoven collects a significant amount of data, such as information on the city's geography, demography, citizen surveys, and up to real-time traffic or air quality measurements. Much of this information is available to the public and presented online in various interactive visualizations. However, this data is not being fully used to address important city questions, provide citizens with insights about their city, or inform policy decisions. In particular, there is a lack of visualizations and analyses that incorporate multiple variables representing various perspectives on the city, like people, environment, infrastructure, and economy. Although city digital twinning is a promising approach towards bringing these perspectives together, the high data volumes and level of detail make city statistics analysis difficult. To address this gap, we assembled a geospatial grid dataset that maps 34 representative city-data variables onto a grid of 0.001 degrees latitude by 0.001 degrees longitude. This creates a common ground where a lightweight systemic view of the city can emerge. We also show two examples of how using this dataset for multivariate analysis of city data can lead to new and more nuanced insights than by analysing one or two variables at a time.
{"title":"A mixed data grid approach for systemic city questions","authors":"T. Langhorst, S. Orzan, Teade Punter, Bernd-Jan Witkamp","doi":"10.1145/3597064.3597321","DOIUrl":"https://doi.org/10.1145/3597064.3597321","url":null,"abstract":"As many cities do, Eindhoven collects a significant amount of data, such as information on the city's geography, demography, citizen surveys, and up to real-time traffic or air quality measurements. Much of this information is available to the public and presented online in various interactive visualizations. However, this data is not being fully used to address important city questions, provide citizens with insights about their city, or inform policy decisions. In particular, there is a lack of visualizations and analyses that incorporate multiple variables representing various perspectives on the city, like people, environment, infrastructure, and economy. Although city digital twinning is a promising approach towards bringing these perspectives together, the high data volumes and level of detail make city statistics analysis difficult. To address this gap, we assembled a geospatial grid dataset that maps 34 representative city-data variables onto a grid of 0.001 degrees latitude by 0.001 degrees longitude. This creates a common ground where a lightweight systemic view of the city can emerge. We also show two examples of how using this dataset for multivariate analysis of city data can lead to new and more nuanced insights than by analysing one or two variables at a time.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132659168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A generalized indoor light sensor can provide information to build and monitor indoor lighting arrangement that is aesthetically pleasing and conforming to the requirements set forth by the inhabitants. However, the identification of the surrounding lighting environment from the sensed parameters has some limitations and challenges. Till-to-date, classifiers are designed to identify only a single source, even in a multi-source environment. Classification based only on sensed values can be imperfect, as multi-type sources can share common parameters, or readings from a single source can fluctuate over time. The classification performances are mostly evaluated in controlled environments. In this work, we use a customised Bluetooth Low Energy (BLE) based light sensor that can sense and advertise major lighting parameters as instructed. Based on sensed parameters and adopting several Machine Learning (ML) and Neural Network (NN) based models off-board, we try to identify the singular and mixed presence of the four dissimilar types of sources: Incandescent, LED, CFL, and Sunlight in indoor surroundings. Off-board identification can get challenging where packet loss scenario is common. For that, we study how IoT devices with superior computational capability can utilise dimensional reduction techniques to minimize the required on-air traffic for classification. We then test classifiers with all those approaches both in controlled environments and real-world testbeds. The result shows that our best model can detect lighting environments with an accuracy of up to 98.22% in the controlled scenario and 83.33% in real-world testbeds.
{"title":"Sensing Indoor Lighting Environments and Analysing Dimension Reduction for Identification","authors":"Tushar Routh, Nurani Saoda, Md Fazlay Rabbi Masum Billah, Nabeel Nasir, Brad Campbell","doi":"10.1145/3597064.3597341","DOIUrl":"https://doi.org/10.1145/3597064.3597341","url":null,"abstract":"A generalized indoor light sensor can provide information to build and monitor indoor lighting arrangement that is aesthetically pleasing and conforming to the requirements set forth by the inhabitants. However, the identification of the surrounding lighting environment from the sensed parameters has some limitations and challenges. Till-to-date, classifiers are designed to identify only a single source, even in a multi-source environment. Classification based only on sensed values can be imperfect, as multi-type sources can share common parameters, or readings from a single source can fluctuate over time. The classification performances are mostly evaluated in controlled environments. In this work, we use a customised Bluetooth Low Energy (BLE) based light sensor that can sense and advertise major lighting parameters as instructed. Based on sensed parameters and adopting several Machine Learning (ML) and Neural Network (NN) based models off-board, we try to identify the singular and mixed presence of the four dissimilar types of sources: Incandescent, LED, CFL, and Sunlight in indoor surroundings. Off-board identification can get challenging where packet loss scenario is common. For that, we study how IoT devices with superior computational capability can utilise dimensional reduction techniques to minimize the required on-air traffic for classification. We then test classifiers with all those approaches both in controlled environments and real-world testbeds. The result shows that our best model can detect lighting environments with an accuracy of up to 98.22% in the controlled scenario and 83.33% in real-world testbeds.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131456828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bhawana Chhaglani, Camellia Zakaria, Jeremy Gummeson, P. Shenoy
The exploration of our envisioned system, BreathEasy, offers the alternative utilization of ambient acoustic sensing techniques to promote user awareness and ensure healthy indoor environments. There has been a renewed interest in providing optimal ventilation indoors after the pandemic. While increasing ventilation at all times leads to high energy consumption, prior work uses occupancy-based or air quality-based approaches to modulate ventilation. However, risk assessment is very complex and requires information about activities performed by occupants, space distribution among occupants, and wearing of masks, on top of other indoor environmental factors such as ventilation rate, air filtration, and room dimensions. Here, we investigate the feasibility of using acoustic sensing mechanisms to produce key parameters essential for airborne transmission risk assessment of occupants in indoor spaces.
{"title":"BreathEasy: Exploring the Potential of Acoustic Sensing for Healthy Indoor Environments","authors":"Bhawana Chhaglani, Camellia Zakaria, Jeremy Gummeson, P. Shenoy","doi":"10.1145/3597064.3597338","DOIUrl":"https://doi.org/10.1145/3597064.3597338","url":null,"abstract":"The exploration of our envisioned system, BreathEasy, offers the alternative utilization of ambient acoustic sensing techniques to promote user awareness and ensure healthy indoor environments. There has been a renewed interest in providing optimal ventilation indoors after the pandemic. While increasing ventilation at all times leads to high energy consumption, prior work uses occupancy-based or air quality-based approaches to modulate ventilation. However, risk assessment is very complex and requires information about activities performed by occupants, space distribution among occupants, and wearing of masks, on top of other indoor environmental factors such as ventilation rate, air filtration, and room dimensions. Here, we investigate the feasibility of using acoustic sensing mechanisms to produce key parameters essential for airborne transmission risk assessment of occupants in indoor spaces.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121726622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emilio Graciliano Ferreira Mercuri, Isadora Bergami, Steffen Manfred Noe, H. Junninen, U. Norbisrath
Particulate matter (PM) is a major air pollutant that can have adverse effects on human health, especially for vulnerable populations such as children, the elderly, and those with respiratory or cardiovascular conditions. This study presents a method for prediction of particulate matter concentration with aerodynamic diameter smaller then 10 μm (PM10) in an urban environment. Meteorological data and vehicle flow data from an urban road in Curitiba, Brazil, were used. The air quality was analyzed in two monitoring points located 1 km apart, the sampling points are named Politécnico and Perkons, where SDS011 optical sensors were installed. The prediction was based on the machine learning algorithm Random Forest (RF). The baseline concentration was a dataset from historical records of particulate matter measurements from monitoring stations in Curitiba. Several scenarios were tested and it was concluded that the daily time scale presents the best performance in PM10 prediction, with 80.42% accuracy, using the baseline and PM10 Perkons as descriptors. The most important meteorological variables for the prediction were: air temperature (°C), wind speed (m/s), and wind gust (m/s). Throughout the day there were two peaks with large amounts of pollutants in the air, near 8:00 am and 6:00 pm, times when there are the largest flows of vehicles circulating on the road. The Random Forest algorithm proved to be a good estimator of PM concentration, which is a proxy for air pollution.
{"title":"Prediction of particulate matter concentration in urban environment using Random Forest","authors":"Emilio Graciliano Ferreira Mercuri, Isadora Bergami, Steffen Manfred Noe, H. Junninen, U. Norbisrath","doi":"10.1145/3597064.3597335","DOIUrl":"https://doi.org/10.1145/3597064.3597335","url":null,"abstract":"Particulate matter (PM) is a major air pollutant that can have adverse effects on human health, especially for vulnerable populations such as children, the elderly, and those with respiratory or cardiovascular conditions. This study presents a method for prediction of particulate matter concentration with aerodynamic diameter smaller then 10 μm (PM10) in an urban environment. Meteorological data and vehicle flow data from an urban road in Curitiba, Brazil, were used. The air quality was analyzed in two monitoring points located 1 km apart, the sampling points are named Politécnico and Perkons, where SDS011 optical sensors were installed. The prediction was based on the machine learning algorithm Random Forest (RF). The baseline concentration was a dataset from historical records of particulate matter measurements from monitoring stations in Curitiba. Several scenarios were tested and it was concluded that the daily time scale presents the best performance in PM10 prediction, with 80.42% accuracy, using the baseline and PM10 Perkons as descriptors. The most important meteorological variables for the prediction were: air temperature (°C), wind speed (m/s), and wind gust (m/s). Throughout the day there were two peaks with large amounts of pollutants in the air, near 8:00 am and 6:00 pm, times when there are the largest flows of vehicles circulating on the road. The Random Forest algorithm proved to be a good estimator of PM concentration, which is a proxy for air pollution.","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132416120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","authors":"","doi":"10.1145/3597064","DOIUrl":"https://doi.org/10.1145/3597064","url":null,"abstract":"","PeriodicalId":362420,"journal":{"name":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133361283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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