Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network
TL;DR: In this article, a wireless network of low-cost particle sensors that can be deployed indoors was developed to overcome the well-known limitations of low sensitivity and poor signal quality associated with low cost sensors, a sliding window and a low pass filter were developed to enhance the signal quality.
About: This article is published in Building and Environment.The article was published on 2018-01-01. It has received 78 citations till now. The article focuses on the topics: Wireless sensor network.
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TL;DR: The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications, and provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions.
Abstract: Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM2.5) has, to date, been limited due to the cost and logistics involved with traditional monitoring approaches. New miniaturized particle sensors are a potential strategy to gather more time- and spatially-resolved data, to address data gaps in regions with limited monitoring and to address important air quality research priorities in a more cost-effective manner. This work presents field evaluations and lab testing of three models of low-cost ( 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R2 = ~0, < 40 μg m-3). The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications (< ~250 μg m-3). These field- testing results provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions. The inconsistent performance results underscore the need for rigorous evaluation of optical particle sensors in the laboratory and in diverse field environments.
96 citations
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..., 2015), to map indoor pollution concentrations (Li et al., 2018), to collect personal exposure data (Steinle et al....
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...…2014; Gao et al., 2015; Zikova et al., 2017), to attribute sources of pollutants (Heimann et al., 2015), to map indoor pollution concentrations (Li et al., 2018), to collect personal exposure data (Steinle et al., 2015; Lewis and Edwards, 2016; Zhang et al., 2017), to collect mobile monitoring…...
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TL;DR: In this article, the authors review the needs and challenges when trying to get high-quality data from low-cost sensors and present a set of best practices to follow to obtain high quality data from these sensors.
89 citations
TL;DR: In this article, the performance of nine low-cost PM monitors (AirVisual, Alphasense, APT, Awair, Dylos, Foobot, PurpleAir, Wynd, and Xiaomi) in a chamber containing a well-defined aerosol was assessed.
Abstract: Due to their affordability, compact size, and moderate accuracy, low-cost sensors have been studied extensively in recent years Different manufacturers employ different calibration methodologies and provide users with calibration factors for their models This study assessed the performance of nine low-cost PM monitors (AirVisual, Alphasense, APT, Awair, Dylos, Foobot, PurpleAir, Wynd, and Xiaomi) in a chamber containing a well-defined aerosol A GRIMM and a SidePak were used as the reference instruments The monitors were divided into two groups according to their working principle and data reporting format, and a linear correlation factor for the PM25 mass concentration measurement was calculated for each monitor Additionally, the differences between the mass concentrations reported by the various monitors and those measured by the reference instruments were plotted against their average before and after user calibration to demonstrate the degree of improvement possible with calibration Bin-specific calibration was also performed for monitors reporting size distributions to demonstrate coincidence errors that could bias the results Since monitors designed for residential use often display the air quality index, typically illustrating it with a simplified, color-coded index, the color schemes of various monitors were evaluated against the US EPA regulation to determine whether they could convey the overall air quality accurately and promptly Although these residential monitors indicated the air quality moderately well, their differing color schemes made the evaluation difficult and potentially inaccurate Altogether, the tested monitors offer low-cost sensors in packages that are convenient for use and ready for deployment without additional assembly However, to improve the accuracy of the measurements, user-defined calibration for the target PM source is still recommended
79 citations
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...Some of these studies have explored in depth the algorithms for organizing sensor data and extracting the maximum effective information (He et al., 2018; Li et al., 2018)....
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...…been deployed in households, meeting rooms, factories, cities, etc. to monitor the dynamic process of pollution events with high spatiotemporal resolution (Kim et al., 2010; Kim et al., 2014; Rajasegarar et al., 2014; Leavey et al., 2015; Patel et al., 2017; Jeon et al., 2018; Li et al., 2018)....
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...to monitor the dynamic process of pollution events with high spatiotemporal resolution (Kim et al., 2010; Kim et al., 2014; Rajasegarar et al., 2014; Leavey et al., 2015; Patel et al., 2017; Jeon et al., 2018; Li et al., 2018)....
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TL;DR: An extensive review of the low-cost particulate matter sensors currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests shows that most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99.
Abstract: The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.
73 citations
TL;DR: A deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (L STM) network, and attention-based network, for urban PM 2.5 concentration prediction with the highest performance is proposed.
Abstract: Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.
69 citations
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...However, it is inevitable for the government to bear a significant financial burden because of expensive equipment [3,4]....
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References
More filters
23,905 citations
TL;DR: It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality.
Abstract: In 2004, the first American Heart Association scientific statement on “Air Pollution and Cardiovascular Disease” concluded that exposure to particulate matter (PM) air pollution contributes to card...
5,227 citations
Book•
01 Jan 1998
TL;DR: This paper aims to provide a history of fuzzy logic in information handling and geostatistics and some of the techniques used to deal with fuzzy logic problems.
Abstract: 1. Geographical Information: Society, Science, and Systems 2. Data models and axioms: Formal abstractions of reality 3. Geographical Data in the Computer 4. Data input, verification, storage and output 5. Creating continuous surfaces from point data 6. Optimal interpolation using geostatistics 7. The analysis of discrete entities in space 8. Spatial analysis using continuous fields 9. Errors and quality control 10. Error propagation in numerical modelling 11. Fuzzy sets and fuzzy geographical objects 12. Current issues and trends in GIS APPENDIX 1 GLOSSARY OF TERMS APPENDIX 2 A SELECTION OF WORLD WIDE WEB GEOGRAPHY AND GIS SERVERS APPENDIX 3 EXAMPLE DATA SETS
3,871 citations
TL;DR: Fine particulate air pollution is a risk factor for cause-specific cardiovascular disease mortality via mechanisms that likely include pulmonary and systemic inflammation, accelerated atherosclerosis, and altered cardiac autonomic function.
Abstract: Background— Epidemiologic studies have linked long-term exposure to fine particulate matter air pollution (PM) to broad cause-of-death mortality. Associations with specific cardiopulmonary diseases might be useful in exploring potential mechanistic pathways linking exposure and mortality. Methods and Results— General pathophysiological pathways linking long-term PM exposure with mortality and expected patterns of PM mortality with specific causes of death were proposed a priori. Vital status, risk factor, and cause-of-death data, collected by the American Cancer Society as part of the Cancer Prevention II study, were linked with air pollution data from United States metropolitan areas. Cox Proportional Hazard regression models were used to estimate PM-mortality associations with specific causes of death. Long-term PM exposures were most strongly associated with mortality attributable to ischemic heart disease, dysrhythmias, heart failure, and cardiac arrest. For these cardiovascular causes of death, a 10-...
2,530 citations
TL;DR: The role of important factors such as solution ionic strength, pH, and particle surface chemistry that control nanoparticle dispersion was examined in this article, where the size and zeta potential of four TiO2 and three quantum dot samples dispersed in different solutions (including one physiological medium) were characterized.
Abstract: Characterizing the state of nanoparticles (such as size, surface charge, and degree of agglomeration) in aqueous suspensions and understanding the parameters that affect this state are imperative for toxicity investigations. In this study, the role of important factors such as solution ionic strength, pH, and particle surface chemistry that control nanoparticle dispersion was examined. The size and zeta potential of four TiO2 and three quantum dot samples dispersed in different solutions (including one physiological medium) were characterized. For 15 nm TiO2 dispersions, the increase of ionic strength from 0.001 M to 0.1 M led to a 50-fold increase in the hydrodynamic diameter, and the variation of pH resulted in significant change of particle surface charge and the hydrodynamic size. It was shown that both adsorbing multiply charged ions (e.g., pyrophosphate ions) onto the TiO2 nanoparticle surface and coating quantum dot nanocrystals with polymers (e.g., polyethylene glycol) suppressed agglomeration and stabilized the dispersions. DLVO theory was used to qualitatively understand nanoparticle dispersion stability. A methodology using different ultrasonication techniques (bath and probe) was developed to distinguish agglomerates from aggregates (strong bonds), and to estimate the extent of particle agglomeration. Probe ultrasonication performed better than bath ultrasonication in dispersing TiO2 agglomerates when the stabilizing agent sodium pyrophosphate was used. Commercially available Degussa P25 and in-house synthesized TiO2 nanoparticles were used to demonstrate identification of aggregated and agglomerated samples.
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