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Field Assessment of the Village Green Project: An Autonomous Community Air Quality Monitoring System
Abstract
Continuous, long-term, and time-resolved measurement of outdoor air pollution has been limited by logistical hurdles and resource constraints. Measuring air pollution in more places is desired to address community concerns regarding local air quality impacts related to proximate sources, to provide data in areas lacking regional air monitoring altogether, or to support environmental awareness and education. This study integrated commercially available technologies to create the Village Green Project (VGP), a durable, solar-powered air monitoring park bench that measures real-time ozone, PM2.5, and meteorological parameters. The data are wirelessly transmitted via cellular modem to a server, where automated quality checks take place before data are provided to the public nearly instantaneously. Over 5500 h of data were successfully collected during the first ten months of pilot testing in Durham, North Carolina, with about 13 days (5.5%) of downtime because of low battery power. Additional data loss (4–14% depending on the measurement) was caused by infrequent wireless communication interruptions and instrument maintenance. The 94.5% operational time via solar power was within 1.5% of engineering calculations using historical solar data for the location. The performance of the VGP was evaluated by comparing the data to nearby air monitoring stations operating federal equivalent methods (FEM), which exhibited good agreement with the nearest benchmark FEMs for hourly ozone (r2 = 0.79) and PM2.5 (r2 = 0.76).
... or package manufacturer which targets many common pollutants: CO, CO2, NO, NO2, O3, SO2, VOC, and PM in addition to less-commonly studied H2S. Data portal and API access are only available to subscribers (AQMesh, accessed 9/18/2023). The AQMesh sensor packages have been used in a wide array of campaign-based research studies (Castell et. al., 2018;Jiao et. al., 2015;Rodríguez et. al., 2020). One study reported in 2017 that the sensors were not yet ready for research-grade applications (Castell et. al., 2017), zeroing in on the importance of proper calibration. 685 ...
... ckages campaign-style in a handful of US cities, including: Durham, North Carolina; Houston, Texas; Washington, DC; Kansas City, KS; Philadelphia, PA; Oklahoma City, OK; Hartford, CT; and Chicago, IL (US EPA Village Green Project, accessed 9/18/2023). Sensor data was compared with that of nearby regulatory grade monitors to ensure data 720 quality (Jiao et. al., 2015). ...
We reviewed 60 sensor networks and 15 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe, but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM), and geographic coverage gaps (the global south, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type: quasi-permanent (long-term) and campaign (short to medium-term) and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways, and aid end users in finding low-cost sensor datasets that meet their needs.
... Governmental operators of routine air monitoring networks are challenged to quickly meet these needs with conventional air monitoring technologies, given the level of cost and effort to initiate and sustain monitoring. An alternative option is a stationary measurement system which integrates commercially available mid-cost instruments, wireless telemetry and solar power for placement in a community (Jiao et al., 2015). These alternative platforms, such as the Village Green Project (VGP) system may represent a compromise between higher density low-cost sensors with higher risk of measurement errors and conventional monitoring stations. ...
... The VGP system was initially developed by the U.S. Environmental Protection Agency to provide real-time outdoor air pollution measurements for community awareness with minimal requirements of operational support. The detailed description of the system has been reported previously (Jiao et al., 2015). The first overseas operation of the instrumented platform was established in Hong Kong as a cooperative agreement with the US Environmental Protection Agency (USEPA), Hong Kong Environmental Protection Department (HKEPD). ...
Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R² = 0.74 for PM2.5 and R² = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.
... However, the above standard instruments are not suitable for these observations because of their relatively high cost, large size, high power consumption, and low temporal resolution (typically >1 h). The development and application of a low-cost palm-sized PM 2.5 sensor, which can measure PM 2.5 mass concentrations precisely and accurately, is important to achieve high-density multi-point observations Lewis and Edwards 2016;Jiao et al. 2015Jiao et al. , 2016Mueller et al. 2016;Patel et al. 2017). A palm-sized PM 2.5 sensor with low power consumption and high temporal resolution can also be applied to mobile measurements for personal exposure studies (Steinle et al. 2015) and to airborne measurements using balloon sonde and unmanned aircraft (Alvarado et al. 2015). ...
... Recently, optical sensors have drawn more attention because of their potential to reduce their size and cost. Studies on the development and/or evaluation of a variety of sizes (from palm-sized to desk-top) of optical PM sensors, including TSI DustTrak, TSI Sidepak, Thermo Scientific DataRAM, the Dylos DC series, Met One Aerocet, the Plantower PMS series, the Shinyei PPD series, the Samyoung (Syhitech) DSM series, and the Sharp GP and DP series, have been reported (Wallace et al. 2011;Northcross et al. 2013;Holstius et al. 2014;Williams et al. 2014;Dacunto et al. 2015;Austin et al. 2015;Wang et al. 2015;Gao et al. 2015;Jiao et al. 2015Jiao et al. , 2016Sousan et al. 2016;Han et al. 2016;Manikonda et al. 2016;Zikova et al. 2017;Kelly et al. 2017). However, the linearity and/or accuracy of some currently available sensors are not necessarily high under specific conditions, especially when the sensor was designed to detect light scattering from particle ensembles. ...
A new palm-sized optical PM2.5 sensor has been developed and its performance evaluated. The PM2.5 mass concentration was calculated from the distribution of light scattering intensity by considering the relationship between scattering intensity and particle size. The results of laboratory tests suggested that the sensor can detect particles with diameters as small as ∼0.3 µm and can measure PM2.5mass concentrations as high as ∼600 µg/m³. Year-round ambient observations were conducted at four urban and suburban sites in Fukuoka, Kadoma, Kasugai, and Tokyo, Japan. Daily averaged PM2.5 mass concentration data from our sensors were in good agreement with corresponding data from the collocated standard instrument at the Kadoma site, with slopes of 1.07–1.16 and correlation coefficients (R) of 0.90–0.91, and with those of the nearest observatories of the Ministry of the Environment of Japan, at 1.7–4.1 km away from our observation sites, with slopes of 0.97–1.23 and R of 0.89–0.95. Slightly greater slopes were observed in winter than in summer, except at Tokyo, which was possibly due to the photochemical formation of relatively small secondary particles. Under high relative humidity conditions (>70%), the sensor has a tendency to overestimate the PM2.5 mass concentrations compared to those measured by the standard instruments, except at Fukuoka, which is probably due to the hygroscopic growth of particles. This study demonstrates that the sensor can provide reasonable PM2.5 mass concentration data in urban and suburban environments and is applicable to studies on the environmental and health effects of PM2.5.
Copyright © 2018 American Association for Aerosol Research
... Because many repeated samples were recorded along the same route in this study, normalized spatial data were able to identify air pollution patterns; however, the authors acknowledge that using a slightly higher cost system (e.g. The Village Green Project [32]) in fewer locations would be a possible alternative. Such a method may employ a network of stationary monitors that are capable of obtaining longer periods of data, but they may be less informative of the local spatial tendencies of air pollution. ...
... Low-cost devices similar to the SCK can have great value if proper precautions are taken (e.g. frequent calibration [5], firmware and/or physical components used to avoid meteorological influences [31], data processing quality control [32]). The issues identified in this study demonstrate where basic improvements can be made in the full-engineered sensor system (e.g. ...
The complex nature of air pollution in urban areas prevents traditional monitoring techniques from obtaining measurements representative of true human exposure. The current study assessed the capability of low-cost mobile monitors to acquire useful data in a city without a monitoring network in place (Lubbock, Texas) using a bicycle platform. The monitoring campaign resulted in 30 days of data along a 13.4 km fixed concentric route. Due to high sensitivities to airflow, the apparent wind velocity was accounted for throughout the route. The data were also normalized into percentiles in order to visualize spatial patterns.
The highest estimated pollution levels were located near frequently busy intersections and roads; however, sensor issues resulted in lower confidence. Additional research is needed concerning the appropriate use of low-cost metal oxide sensors for citizen science applications, as measurements can be misleading if the user is unaware of sensors specifications. The simultaneous use of several low-cost mobile platforms, rather than a single platform, as well as the use of high-end cases, are recommended to create a more robust spatial analysis. The issues addressed from this research are important to understand for accurate and beneficial application of low-cost gaseous monitors for citizen science.
... Epidemiological Relationship between IAQ and Health Issues[131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146][147] heart or lung illness, nonfatal heart attacks, irregular heartbeats, worsened asthma, impaired lung function, and a rise in respiratory symptoms including coughing or trouble breathing.At high quantities, it shortens breath and irritates the mucous membranes of the nose, throat, and eyes. Long-term inhalation of nitrogen dioxide can cause lung damage. ...
Indoor air quality and public health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spikes during the time of pandemic and epidemics when indoor air pollution becomes a threat to living beings especially human beings. Indoor diseases are hard to diagnose if they are due to the indoor environmental conditions. A major challenge was observed in establishing a baseline between the indoor air quality sensors and associated diseases. In this work, 10,000+ articles from top literature databases were reviewed using bibliometric analysis to formulate indoor air quality sensors and diseases correlation rubrics to critically review 500+ articles. A set of 200+ articles were selected based on for detailed study based on seven bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA defined principles. This review has been conducted to assist end-users, public health facilities, state agencies, researchers, scientists and air quality protection agencies.
... Air quality measurement with reference method or research grade instrumentation can be expensive. There is growing interest in the role of so-called low-cost sensors as a means to be able to deploy more sensors to characterize spatial variability, and to be able to do so for long periods of time to better quantify temporal variability (e.g., Jiao et al. 2015). Given concerns about accuracy, interference, and other data quality limitations, it is unlikely that such sensors can completely replace reference methods. ...
Introduction:
Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development.
The key pollutants that are the key focus of this work include nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM2.5; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O3). NOx, CO, and BC are tracers of vehicle emissions and dispersion. PM2.5 is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM2.5 and UFP concentrations. O3 concentrations are influenced by interaction with NOx near the roadway. Nitrogen dioxide (NO2), CO, PM2.5, and O3 are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work.
Methods:
The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection.
The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM2.5 at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites.
Results:
We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system.
At the freeway site, we measured the spatial gradient of NOx, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants.
At the urban site, we measured daily average concentrations of nitric oxide (NO), NOx, O3, and PM2.5 at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O3, PM2.5, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution.
Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R2) was 0.61 for hourly NOx concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NOx at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NOx at the freeway site and for UFPs and O3 at the urban site pedestrian transect.
Conclusions:
Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.
... These limitations create a large monetary barrier for communities and individuals interested in providing representation for themselves and those around them. As a result, many individuals go unrepresented as to their exposure (Jiao et al., 2015). There has been a trend in developing low-cost aerosol sensors to open the description of exposures to individuals to represent their indoor air quality (Popoola et al., 2018). ...
Low-cost aerosol sensors open routes to exposure assessment and air monitoring in various indoor and outdoor environments. This study evaluated the accuracy of GeoAir2-a recently developed low-cost particulate matter (PM) monitor-using two types of aerosols (salt and dust), and the effect of changes in relative humidity on its measurements in laboratory settings. For the accuracy experiments, 32 units of GeoAir2 were used, and for the humidity experiments, 3 units of GeoAir2 were used, alongside the OPC-N3 low-cost sensor and MiniWRAS reference instrument. The normal distribution of slopes between the salt and dust aerosols was compared for the accuracy experiments. In addition, the performance of GeoAir2 in indoor environments was evaluated compared to the pDR-1500 reference instrument by collocating GeoAir2 and pDR-1500 at three different homes for five days. For salt and dust aerosols smaller than 2.5 µm (PM2.5), both GeoAir2 (r = 0.96-0.99) and OPC-N3 (r = 0.98-0.99) were highly correlated with the MiniWRAS reference instrument. However, GeoAir2 was less influenced by changes in humidity than OPC-N3. While GeoAir2 reported an increase in mass concentrations ranging from 100% to 137% for low and high concentrations, an increase between 181% and 425% was observed for OPC-N3. The normal distribution of the slopes for the salt aerosols was narrower than dust aerosol, which shows closer slope similarities for salt aerosols. This study also found that GeoAir2 was highly correlated with the pDR-1500 reference instrument in indoor environments (r = 0.80-0.99). These results demonstrate potential for GeoAir2 for indoor air monitoring and exposure assessments.
... They used several air quality parameters that represent the complex mixture of pollutants emitted by motor vehicles. Jiao et al. [10] developed an integrated air and weather measurement system that was more easily deployed in outdoor community spaces, minimizes operator maintenance, and provides real-time, quality-checked data to the public. A system called MAAV was developed by Moore et al. [18] to perform three tasks-measure air quality, annotate data streams and visualize realtime PM2.5 1 levels. ...
As cities continue to grow globally, air pollution is increasing at an alarming rate, causing a significant negative impact on public health. One way to affect the negative impact is to regulate the producers of such pollution through policy implementation and enforcement. CleanAirNowKC (CAN-KC) is an environmental justice organization based in Kansas City (KC), Kansas. As part of their organizational objectives, they have to date deployed nine PurpleAir air quality sensors in different locations about which the community has expressed concern. In this paper, we have implemented an interactive map that can help the community members to monitor air quality efficiently. The system also allows for reporting and tracking industrial emissions or toxic releases, which will further help identify major contributors to pollution. These resources can serve an important role as evidence that will assist in advocating for community-driven just policies to improve the air quality regulation in Kansas City.
... While the majority of environmental sensor networkrelated projects is driven by the academia (i.e. government funding), there are also commercial and/or crowd-funded projects, that have been introduced to the public. The most profound of the projects that have been presented are the US EPA funded CAIRSENSE project, that focused on the performance evaluation of different sensors [2], the large scaled, multinational Citi-Sense project, that focused on ambient air quality, indoor environment at schools, and the quality of urban spaces [3] and the Village-Green project, that mainly focused on the power consumption of the wireless monitoring platform [4]. Other projects that have been widely used are the commercially funded AirVisual and Airpurple projects. ...
In the present paper, a new, low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises dedicated low cost, low power, hardware and the associated embedded software that enables measurement of particles (PM2.5 and PM10), NO, CO, CO2 and O3 concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensors measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the newly developed platform took place in Thessaloniki, Greece, where multiple devices were installed in various buildings in the city. Preliminary results from the pilot testing period are provided with focus on COVID-19 impact on air quality.
... While the majority of environmental sensor network-related projects is driven by the academia (i.e. government funding), there are also commercial and/or crowd-funded projects, that have been introduced to the public. The most profound of the projects that have been presented are the US EPA funded CAIRSENSE project, that focused on the performance evaluation of different sensors [2], the large scaled, multi-national Citi-Sense project, that focused on ambient air quality, indoor environment at schools, and the quality of urban spaces [3] as well as the Village-Green project, that mainly focused on the power consumption of the wireless monitoring platform [4]. Other projects that have been widely used are the commercially funded AirVisual and Airpurple projects. ...
The recent emergence of low-cost sensor technologies, measuring various air pollutants, enables the real-time collection of air pollution data, with high spatio-temporal resolution. In our recent study, we developed and deployed low-cost air quality monitoring sensors and distributed them to citizens of Thessaloniki, Greece. The aim of this study was to provide near real-time air pollution measurements that will allow the better representation of the air pollution levels in the greater area of Thessaloniki. In the current work, we present the low-cost adjustable wireless sensor platform that has been developed for air pollution monitoring in urban areas. The platform can take sensor measurements down to one sample per second and is suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The paper aims to contribute to the area of low-cost, distributed sensor networks for Environmental Intelligence applications.
... While the majority of environmental sensor network-related projects is driven by the academia (i.e. government funding), there are also commercial and/or crowd-funded projects, that have been introduced to the public. The most profound of the projects that have been presented are the US EPA funded CAIRSENSE project, that focused on the performance evaluation of different sensors [2], the large scaled, multinational Citi-Sense project, that focused on ambient air quality, indoor environment at schools, and the quality of urban spaces [3] and the Village-Green project, that mainly focused on the power consumption of the wireless monitoring platform [4]. Other projects that have been widely used are the commercially funded AirVisual and Airpurple projects. ...
In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO 2 and O 3 concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.
... While the majority of environmental sensor network-related projects is driven by the academia (i.e. government funding), there are also commercial and/or crowd-funded projects, that have been introduced to the public. The most profound of the projects that have been presented are the US EPA funded CAIRSENSE project, that focused on the performance evaluation of different sensors [2], the large scaled, multi-national Citi-Sense project, that focused on ambient air quality, indoor environment at schools, and the quality of urban spaces [3] as well as the Village-Green project, that mainly focused on the power consumption of the wireless monitoring platform [4]. Other projects that have been widely used are the commercially funded AirVisual and Airpurple projects. ...
The recent emergence of low-cost sensor technologies, measuring various air pollutants, enables the real-time collection of air pollution data, with high spatio-temporal resolution. In our recent study, we developed and deployed low-cost air quality monitoring sensors and distributed them to citizens of Thessaloniki, Greece. The aim of this study was to provide near real-time air pollution measurements that will allow the better representation of the air pollution levels in the greater area of Thessaloniki. In the current work, we present the low-cost adjustable wireless sensor platform that has been developed for air pollution monitoring in urban areas. The platform can take sensor measurements down to one sample per second and is suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The paper aims to contribute to the area of low-cost, distributed sensor networks for Environmental Intelligence applications.
... While the majority of environmental sensor network-related projects is driven by the academia (i.e. government funding), there are also commercial and/or crowd-funded projects, that have been introduced to the public. The most profound of the projects that have been presented are the US EPA funded CAIRSENSE project, that focused on the performance evaluation of different sensors [2], the large scaled, multinational Citi-Sense project, that focused on ambient air quality, indoor environment at schools, and the quality of urban spaces [3] and the Village-Green project, that mainly focused on the power consumption of the wireless monitoring platform [4]. Other projects that have been widely used are the commercially funded AirVisual and Airpurple projects. ...
In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power, hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO2 and O3 concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensors measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the newly developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.
... Many of their observations were elevated levels of the parameters over the recommended standards. Different source apportionment (Li et al., 2014;Ma et al., 2016), methodologies (Chen et al. (2016); Shi et al. (2016); Meˇciarová et al. (2017); Lee et al. (2018), and instruments (Jiao et al., 2015;Van den Bossche et al., 2016;Zikova et al., 2017;Lee et al., 2018) have been used to determine and identify air pollution parameters. In Nigeria, many studies have not been done on indoor air pollution in Nigeria, but few literates obtained were done in Enugu (Ezezue and Diogu, 2017), Warri (Akpofure, 2015), Ile -Ife (Afolabi et al., 2016), Ido Ekiti (Ayodele et al., 2016), No report has been found for indoor pollution from Akure. ...
... A pilot station [112] by US EPA-The Village Green-in Durham, North Carolina, has demonstrated the ability to monitor several common air pollutants in real time and make the data available online and accessed by smartphone. The Village Green Project [14,113] model is expanding to other communities across the United States to increase awareness of this new community-based air quality monitoring system developed by EPA. The solar and wind powered station is a park bench structure with instruments that provide minute-to-minute air measurements for nitrogen dioxide, ozone, carbon monoxide, carbon dioxide, particle matter pollution, and weather conditions. ...
... However, recent developments in ozone monitoring technology have made it possible to include instrumentation and continuous data collection in community-and school-based education programs. For example, the U.S. Environmental Protection Agency's (EPA) Village Green Project focuses on the continuous, long-term measurement of outdoor air pollution, including ozone measured with the 2B Technologies Model 106-L Ozone Monitor (Jiao et al., 2015). The data collected are streamed online and updated by the minute, or can be displayed on a smartphone when at a Village Green station. ...
Over the past decade, new and emerging technologies in air pollution instrumentation have made it possible to involve students and citizen scientists in air pollution monitoring. Similarly, advances in data communication and transmission have made it increasingly easy to share and graphically display data. Two educational programs, the Global Ozone (GO3) Project and AQTreks, have used these advances to get air pollution monitors into the hands of thousands of students around the world and to automate data sharing. The pilot project for AQTreks, GO3 Treks, is also discussed. These educational projects began in 2009 with the GO3 Project, a stationary ground-level ozone monitoring project. In the GO3 Project, students and teachers at more than 100 schools from around the world installed ozone and weather monitoring stations at their schools with automatic uploading of their data every 15 min, resulting in more than 12 million ozone measurements along with associated weather data. Over the years, new technologies became available for students to expand their measurements from stationary to mobile platforms. Since 2016, the AQTreks educational program has been developed concurrently with the Personal Air Monitor (PAM), a mobile sensor suite paired with a smartphone app. Complementing the technology are online curricula and other resources for students and citizens to learn about air pollution and climate change. In these projects, a focus on data quality and the careful selection of monitoring technologies have resulted in scientific use of the student-collected data, including their incorporation in several research campaigns that have furthered understanding of ground-level ozone formation. This approach has demonstrated the utility of these types of educational programs both in terms of furthering scientific research and educating the next generation about air quality issues.
... Many of their observations were elevated levels of the parameters over the recommended standards. Different source apportionment ( Li et al., 2014;Ma et al., 2016), methodologies ( Chen et al. (2016); Shi et al. (2016); Meˇciarová et al. (2017); Lee et al. (2018), and instruments ( Jiao et al., 2015;Van den Bossche et al., 2016;Zikova et al., 2017;Lee et al., 2018) have been used to determine and identify air pollution parameters. In Nigeria, many studies have not been done on indoor air pollution in Nigeria, but few literates obtained were done in Enugu ( Ezezue and Diogu, 2017), Warri (Akpofure, 2015), Ile -Ife ( Afolabi et al., 2016), Ido Ekiti ( Ayodele et al., 2016), No report has been found for indoor pollution from Akure. ...
Air quality has been a major concern throughout the world, Nigeria inclusive. The monitoring of air quality involves indoor and outdoor air quality. In this study, our concern was on indoor air quality. The aim of this study was to assess the air quality of residential homes (17), classrooms (3), hospitals (2), offi ces (5), Shops (2), and laboratories (5) in Akure, Nigeria in terms of formaldehyde (HCHO), total volatile organic compound (TVOC), Particulate matter (PM 1.0 ; PM 2.5 , and PM 10). A Multifunction Air Detector was used for the assessment using the manufacturers' procedures and the locations were identifi ed using a Mini GPS. The results revealed as follows: HCHO (0.001-0.030 mg/ m 3), TVOC (0.003-362 mg/m 3), PM 1.0 (004-014 μg/m 3), PM 2.5 (006-020 μg/m 3), and PM 10 (006-022 μg/m 3). The results obtained were below the 24 h pollution recommended standards (0.1 mg/m 3-HCHO; TVOC; 10-20 μ/m 3 PM) of EPA and WHO. Statistically, there were correlations within the pollutants and weather. The Indoor air quality (IAQ) depicted the areas as 'good,' and toxicity potential (TP) were below unity. Although the locations looked safe, it is recommended that constant monitoring of the indoors should be ensured and proper ventilation should be provided.
... Holstius (2014) has argued that as an increasing number of sensors come online among the public, it will support more timely policy decisions made by local governments regarding issues such as mitigation policies and the pricing of traffic congestion in order to reduce air pollution levels. The overall reduction in the cost of particulate matter sensors has stimulated the installation of community-based continuous monitoring systems (Jiao et al. 2015), which are necessary for a community that uses handheld devices to calibrate their equipment on a regular basis. For instance, Jiao et al.'s (2015) study included a pDR-1500 (Thermo Scientific, Waltham, MA, USA) within the package of instruments that they established within a community-based monitoring system. ...
Environmental protection agencies around the globe are establishing different methods for measuring particulates, and then integrating those measurements into a single air quality index with other pollutants. At the same time, scientific inquiry has also shifted to a theory of measurement that incorporates discrete and continuous measurement. This article reviews the relationship between discrete measurements and indices, while also speculating on the way that the continuous measurement of air pollution could stimulate awareness and action. The paper argues that continuous measurement must include the way people of different backgrounds perceive air pollution in their lives. After reviewing the methods of measuring particulates and their inclusion into various indices, the article argues that in order to take action to mitigate the health impacts of air pollution, we must allow for the social perception of air pollution to become entangled within our scientific measurements.
... The requirements of the people are pleasant and fresh air, which has no negative impact on their health (Fanger, 2006). Lee et al. (2018), and instruments (Jiao et al., 2015;Van den Bossche et al., 2016;Zikova et al., 2017;Lee et al., 2018) have been used to determine and identify air pollution parameters. In Nigeria, many studies have not been done on indoor air pollution in Nigeria, but few literates obtained were done in Enugu (Ezezue and Diogu, 2017), Warri (Akpofure, 2015), Ile -Ife (Afolabi et al., 2016), Ido Ekiti (Ayodele et al., 2016), No report has been found for indoor pollution from Akure. ...
Air quality has been a major concern throughout the world, Nigeria inclusive. The monitoring of air quality involves indoor and outdoor air quality. In this study, our concern was on indoor air quality. The aim of this study was to assess the air quality of residential homes (17), classrooms (3), hospitals (2), offices (5), Shops (2), and laboratories (5) in Akure, Nigeria in terms of formaldehyde (HCHO), total volatile organic compound (TVOC), Particulate matter (PM1.0; PM2.5, and PM10). A Multifunction Air Detector was used for the assessment using the manufacturers’ procedures and the locations were identified using a Mini GPS. The results revealed as follows: HCHO (0.001-0.030 mg/m3), TVOC (0.003-362 mg/m3), PM1.0 (004-014 µg/m3), PM2.5 (006-020 µg/m3), and PM10 (006-022 µg/m3). The results obtained were below the 24 h pollution recommended standards (0.1 mg/m3- HCHO; TVOC; 10-20 μ/m3 PM) of EPA and WHO. Statistically, there were correlations within the pollutants and weather. The Indoor air quality (IAQ) depicted the areas as ‘good,’ and toxicity potential (TP) were below unity. Although the locations looked safe, it is recommended that constant monitoring of the indoors should be ensured and proper ventilation should be provided.
... 20 Compact air monitoring systems, with self-contained power and wireless data communications, can allow for nearly autonomous monitoring in remote areas, such as tall towers, and circumvent the need to run long power cables or sampling lines that hindered previous efforts. Jiao et al. 21 demonstrated that a solar-powered UV absorbance-based compact ozone monitor was able to provide accurate readings, despite frequent power cycling and operation in an environment without controlled temperature or humidity. Similar instrumentation deployed on the top of tall building in Hong Kong was also shown to provide accurate data even after being subjected to frequent power cycling, subtropical conditions, and several major weather events, 22 suggesting the feasibility of an instrument package that could provide reliable measurements over a longterm in an autonomous manner. ...
Changing precursor emission patterns in conjunction with stringent health protective air quality standards, necessitate accurate quantification of non-local contributions to ozone pollution at a location due to atmospheric transport, that by nature predominantly occurs aloft nocturnally. Concerted efforts to characterize ozone aloft on a continuous basis to quantify its contribution to ground-level concentrations however are lacking. Applying our classical understanding of air pollution dynamics to analyze variations in widespread surface-level ozone measurements, in conjunction with process-based interpretation from a comprehensive air pollution modeling system and detailed backward-sensitivity calculations that quantitatively link surface-level and aloft pollution, we show that accurate quantification of the amount of ozone in the air entrained from aloft every morning as the atmospheric boundary layer grows is the key missing component for characterizing background pollution at a location, and propose a cost-effective continuous aloft ozone measurement strategy to address critical scientific gaps in current air quality management. Continuous aloft air pollution measurements can cost-effectively be achieved through leveraging advances in sensor technology and proliferation of tall telecommunications masts. Resultant improvements in ozone distribution characterization at 400-500m altitude are estimated to be 3-4 times more effective in characterizing the surface-level daily maximum 8-hour average ozone (DM8O3) than improvements from surface measurements since they directly quantify the amount of pollution imported to a location, and furnish key-missing information on processes and sources regulating background ozone and its modulation of ground-level concentrations. Since >80% of the DM8O3 sensitivity to tropospheric ozone is potentially captured through measurements between 200-1200m altitude (a possible design goal for future remote sensing instrumentation), their assimilation will dramatically improve air quality forecast and health advisories.
... An effective approach to air quality monitoring can provide important information for exposure assessment, epidemiology, air quality management, and environmental equity [13,14], but the extent of fixed-site air pollution measurement is limited by the cost of equipping and maintaining fixed-site air quality monitoring stations (AQMSs) [1,15]. Air pollutant concentration in cities may vary sharply over short distances (∼0.01-1 km) due to the uneven distribution of sources of emissions, dilution, and physicochemical transformation [1,16,17]. ...
Mobile air quality monitoring reports air pollutant concentrations at a high spatiotemporal resolution, enabling the characterization of heterogeneous human exposure and localized pollution hotspots. In this study, on-road concentrations of fine particulate matter (PM2.5) in a high-density urban area in Hong Kong were measured in December 2014 and January 2015 (winter) and June and July 2015 (summer) using a tramcar mobile monitoring platform. We developed a method of mapping the winter and summer on-road PM2.5 concentrations along a tramcar route at a 50-m spatial resolution, using mobile measurements. In addition, the minimum number of days required to precisely estimate on-road PM2.5 concentrations was estimated. The results showed that the on-road PM2.5 concentrations were highly correlated with PM2.5 concentrations measured at a nearby roadside air quality monitoring station (AQMS) in both winter and summer, with Pearson correlation coefficients of 0.89–0.93. The resulting maps of winter and summer on-road PM2.5 concentrations revealed small-scale spatial patterns used to identify more polluted areas. In addition, approximately 12 and 4 days were required to precisely capture spatial patterns of PM2.5 concentrations, with R2 higher than 0.6 in winter and in summer. The findings of this study offer valuable information on air pollution control and exposure reduction by highlighting localized pollution hotspots, and provide insights into the minimum sampling duration for mobile sampling campaigns.
... Public parks, libraries, museums and other locations of high public access linked the stations to local partners devoted to sustainable energy practices, environmental awareness, and educational opportunities. The Village Green has provided a wealth of community-based knowledge and data from these sites are being used to assist the U.S. EPA in establishing short-term data messaging (Jiao et al., 2015). ...
... However, as the measurements and data analysis become more complex, then the cost and inconvenience of using low cost sensors increases. A growing number of sensors are offered commercially, prompting the emergence of methods and studies for benchmarking and evaluating sensors Jerrett et al., 2017;Jiao et al., 2015). Citizen-science users are typically not trained in study design, data collection, data analysis, and data interpretation, prompting the need for information exchange and development of best practices (Clements et al., 2017;Rai et al., 2017). ...
Implications:
Without specific policies to the contrary, fossil fuels are likely to continue to be the major source of on-road vehicle energy consumption. Fuel economy and emission standards are generally effective in achieving reductions per unit of vehicle activity. However, the number of vehicles and miles traveled will increase. Total energy use and emissions depend on factors such as fuels, technologies, land use, demographics, economics, road design, vehicle operation, societal values, and others that affect demand for transportation, mode choice, energy use, and emissions. Thus, there are many opportunities to influence future trends in vehicle energy use and emissions.
... With the recent commercialization of low-cost and easy-to-use devices, susceptible groups and individuals such as children, seniors, asthmatics, pregnant women, and people interested in measuring air pollution in their communities can monitor air quality and assess potential personal exposure. Citizen scientists and community groups have now access to a wealth of information to better understand how air pollution may impact their neighborhoods (Deville Cavellin et al., 2016;Jiao et al., 2015). Air quality sensors deployed near industrial facilities, such as those for fence-line monitoring applications, can provide empirical data to supplement existing ambient air monitoring infrastructure (Pikelnaya et al., 2013). ...
A state-of-the-art integrated chamber system has been developed for evaluating the performance of low-cost air quality sensors. The system contains two professional grade chamber enclosures. A 1.3 m³ stainless-steel outer chamber and a 0.11 m³ Teflon-coated stainless-steel inner chamber are used to create controlled aerosol and gaseous atmospheres, respectively. Both chambers are temperature and relative humidity controlled with capability to generate a wide range of environmental conditions. The system is equipped with an integrated zero-air system, an ozone and two aerosol generation systems, a dynamic dilution calibrator, certified gas cylinders, an array of Federal Reference Method (FRM), Federal Equivalent Method (FEM), and Best Available Technology (BAT) reference instruments and an automated control and sequencing software. Our experiments have demonstrated that the chamber system is capable of generating stable and reproducible aerosol and gas concentrations at low, medium, and high levels. This paper discusses the development of the chamber system along with the methods used to quantitatively evaluate sensor performance. Considering that a significant number of academic and research institutions, government agencies, public and private institutions, and individuals are becoming interested in developing and using low-cost air quality sensors, it is important to standardize the procedures used to evaluate their performance. The information discussed herein provides a roadmap for entities who are interested in characterizing air quality sensors in a rigorous, systematic and reproducible manner.
... While the sensor technologies for particulate matter and ozone are not considered truly low cost (~$6000/each), they do represent mid-tier technologies [55] which are showing good to excellent capabilities for certain attributes. In particular, they are capable of providing extended periods (months) of ambient air quality monitoring using sustainable energy (solar power) with little or no technical support and often with a high degree of agreement with local reference monitoring [56]. Another unique feature of the Village Green is that it was designed to stream data continuously to the public via its webbased data portal. ...
The US Environmental Protection Agency (EPA) and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes) readings from mobile and handheld air sensors with the longer duration averages (hours to days) associated with the National Ambient Air Quality Standards (NAAQS) for the criteria pollutants-particulate matter (PM), ozone, carbon monoxide, lead, nitrogen oxides, and sulfur oxides. Similar issues are equally relevant to the hazardous air pollutants (HAPs) where chemical-specific health effect reference values are the best indicators of exposure limits; values which are often based on a lifetime of continuous exposure. A multi-agency, staff-level Air Sensors Health Group (ASHG) was convened in 2013. ASHG represents a multi-institutional collaboration of Federal agencies devoted to discovery and discussion of sensor technologies, interpretation of sensor data, defining the state of sensor-related science across each institution, and provides consultation on how sensors might effectively be used to meet a wide range of research and decision support needs. ASHG focuses on several fronts: improving the understanding of what hand-held sensor technologies may be able to deliver; communicating what hand-held sensor readings can provide to a number of audiences; the challenges of how to integrate data generated by multiple entities using new and unproven technologies; and defining best practices in communicating health-related messages to various audiences. This review summarizes the challenges, successes, and promising tools of those initial ASHG efforts and Federal agency progress on crafting similar products for use with other NAAQS pollutants and the HAPs. NOTE: The opinions expressed are those of the authors and do not necessary represent the opinions of their Federal Agencies or the US Government. Mention of product names does not constitute endorsement.
... The Village Green Project is an EPA research and public education air monitoring system that uses several lower cost instruments that are placed in a community solar-powered bench. The measurements do not meet regulatory air quality monitoring requirements, but can be explored to study how air pollution changes with time and weather (Jiao et al., 2015). It is important to note that the Village Green benches, while equipped with ozone Federal Equivalent Method (FEM) grade monitors, are not maintained as FEM sites. ...
Air quality sensors are becoming increasingly available to the general public providing individuals and communities with information on fine-scale, local air quality in increments as short as one minute. Current health studies do not support linking 1-minute exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8-hour; 24-hour). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 minute to 1 hour), and the longer-term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings.
This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hour average (ozone) and 24-hour average (PM2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hour averages (ozone) and 24-hour averages (PM2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.
Implications
Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.
... Although the national and city government has progressed to fund expansion of existing high end air quality network monitoring networks in Delhi and other Indian cities, smart cities employing wireless sensor networks (WSNs) with low cost and compact sensors are a future reality for city governments and municipalities around the world. In recent years, there has been a trend worldwide to increase the collection of air quality data beyond fixed monitoring stations through low-power WSNs with small, low-cost sensor nodes at distributed monitoring points for urban, industrial, and community sensing [16,17,18,19]. ...
Monitoring ambient (outdoor) air quality in school surroundings is of great importance for the health of school children. To help schools in smart cities to assess whether ambient air exposure is unhealthy for school children, in this paper, we present a low-cost solar-powered air quality monitoring system based on ZigBee wireless network system technology. The solar powered network sensor nodes can be deployed by schools to collect and report real-time data on carbon monoxide (CO), nitrogen dioxide (NO2), dust particles, temperature, and relative humidity. The proposed system allows schools to monitor air quality conditions on a desktop/laptop computer through an application designed using LabVIEW and provides an alert if the air quality characteristics exceed acceptable levels. The sensor network was successfully tested at the Singapore campus of the University of Newcastle, Australia. The experimental results obtained demonstrate that the sensor network can provide high-quality air quality measurements over a wide range of CO, NO2 and dust concentrations.
... Using networks of these low-cost sensors would enable continuous collection of air pollutant data with high spatial resolution. Several US EPA studies are underway investigating this, including a project with ozone and PM sensors integrated in a park bench [22]. Sensors are also available for the determination of methane, using semiconductor technology as the detection principle [23][24][25] consist of a metal oxide film which is heated by a built-in resistive heater and whose resistivity changes with its oxygen content. ...
In order to expand current atmospheric methane monitoring capabilities, we investigated the performance of an off-the-shelf, low-cost, metal oxide based methane (CH4) gas sensor. A sensor assembly was designed that powered the gas sensor and its resistive heater, provided ancillary temperature (T) and relative humidity (rH) measurements, and controlled sensor read-out and data storage. After calibrating the gas sensor with respect to methane concentration ([CH4]), T, and rH, we were able to estimate the [CH4] of lab air over a period of 31 days for a large range of T and rH conditions with a systematic error of −1.0 ppm and a variable error within ±1.7 ppm. The sensor showed no significant drift in [CH4] estimate. We show that sensor accuracy can likely be improved by optimizing the voltage regulator that powers the gas sensor’s heater, and by measuring and compensating for the difference in partial oxygen pressure of the air that was sampled during calibration and validation experiments. Such improvements are expected to allow the use of the sensor assembly for fence-line methane monitoring, for example near fossil fuel extraction sites. In its current form, the sensor assembly is suitable for detecting fugitive methane from leak-prone equipment.
Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies.
Different integration methods were tested to integrate data from a dynamic road network (ROM) in which pollution measurement sensors were mounted over delivery vans. Two methods were purposely developed, the isoelliptical expansion - ISOE - method accounting for the wind convective transport of pollutants and the modified isoelliptical expansion - MISOE - method in which, furthermore, local specific deviation of the pollution are estimated from historical sequences of pollution levels. The results obtained by these methods were compared with the well-known inverse distance weighted - IDW - method, which is only based on the distance from the interpolation sources. The comparison of the errors between the estimated values and the available measures reveals that the MISOE model provides more accurate estimated values with a low associated error. The ISOE model is more complicate than the IDW but provides better estimations in windy days. The maps of the local adjusting coefficients estimated month by months are able to identify critical areas to address in local environmental policy decisions.
Community science offers unique opportunities for non-professional involvement of volunteers in the scientific process, not just during the data acquisition, but also in other phases, like problem definition, quality assurance, data analysis and interpretation, and the dissemination of results. Moreover, community science can be a powerful tool for public engagement and empowerment during policy formulation. This paper aims to present a pilot study on personal exposure to fine particulate matter (PM2.5) and raises awareness of the hazards of air pollution. As part of data acquisition conducted in 2019, high school students gathered data at their schools, schoolyards, and playgrounds using low-cost monitors AirBeam2. The data was automatically uploaded every second onto the AirCasting mobile app. Besides, a stationary network of air monitors (fixed stations) was deployed in the neighborhood to collect real-time ambient air concentrations of PM2.5. Students involved in the project attended workshops, training sessions, and researched to better understand air pollution, as part of their science class curriculum and portfolio. This air quality monitoring was incorporated into the “Our Air/Nuestro Aire” — El Puente grassroots campaign. The main goals of this campaign included sharing the data collected with the community, engaging academic partners to develop a set of policy and urban design solutions, and to be considered into a 5-point policy platform.
This chapter provides a review of air quality monitoring techniques ranging from traditional to advanced. It highlights types and measurement principles of sensors for the monitoring of particulate matters and gases with their advantages and shortcomings. The chapter provides a detailed account of the case studies of sensor calibrations and applications with online data publicizing, conducted by the Air Quality Research group at the Asian Institute of Technology. The air quality management system uses several technical tools to provide information on the air quality. Key technical tools include air quality monitoring, air pollution emission inventory and air quality modeling. For the environmental monitoring, the wireless sensor network is applied for air quality, water quality and natural disasters such as landslides, forest fires or volcanic eruptions. The use of wireless low‐cost sensors widens the spatial and temporal distributions of the monitoring data which is important for the health effect assessment and overall air quality management.
Air pollution is a major problem in urban areas, where high population density is accompanied with excess anthropomorphic emissions impacting the environment and increasing health effects. Highly accurate air quality monitoring stations have been used to monitor the severity of the problem and warn citizens. However, air quality can vary sharply even within the same city block, and pollution exposure can vary even 30% between individuals living in the same residence. Therefore, a dense deployment of air quality sensors is needed to detect these variations, and protect citizens from overexposure. Low-cost air quality sensors make it possible to densely instrument a city and detect hot spots as they happen. However, thus far limited information exists on their accuracy and practicability. In this paper, we conduct a 44 day measurement campaign to assess performance of low-cost air quality monitors under different environmental conditions. As practical use case we consider pollution hot spot detection. Our results show that the mean error of low-cost sensors is small, but the variation in error is significantly larger than with reference sensors. We also show that the accuracy is sufficient for applications relying on variations in air quality index values, such as hotspot detection.
Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.
The measurement of environmental variables has become a daily problem in recent years. However, the equipment commonly used for these measurements is expensive and bulky, and therefore, it is not possible to have enough spatial resolution. In addition, many of the measurement methods do not provide real-time information to deliver to citizens in a timely manner. In some works, these issues have been handled through the deployment of WSN based on low cost technologies. The improvement of the spatial and temporal resolution implies the increase in the amount of information to be transmitted and stored. For this reason, in this paper is presented a method for data reduction through a dynamic sub-sampling of the measured variable, data fusion from several sensors for the same variable, and data scaling taking into account the the variables range. The reduction of data is implemented to save energy, reduce the transmission time, keep the channel available, and save storage space. The method is validated using a low-cost monitoring station that combines environmental, particulate matter, gas, electromagnetic radiation, and inertial sensors to be transmitted in a 50-byte reduced packet using a LoRa network. The subsampling adjustment was developed for the PM signal. The results show a reduction in the volume of stored data and the relevant information is not affected. The transmitted data packet can be reduced from 96 to 50 bytes, and sampling can be reduced to 4% of the original sampling without affecting the trend of the PM information.
Rigorous and rapid assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we use hierarchical modeling to enable real‐time forecasting of 8‐hr average ozone exposure. This contrasts with customary retrospective analysis of exposure data. Specifically, our contribution is to show how incorporating temperature data in addition to the observed ozone can significantly improve forecast accuracy, as measured by predictive performance and empirical coverage. We adopt two‐stage autoregressive models, also introducing periodicity and heterogeneity while still maintaining our objective of forecasting in real time. The entire effort is illustrated through modeling data collected at the Village Green monitoring station in Durham, North Carolina.
Climate change is a scientifically and chemically complex issue with both global and local impact. Public understanding and awareness of climate change are crucial for building support to address causes and impacts; unfortunately, the vast majority of Americans do not understand the basic science behind climate change. This trend applies even to first-year college students in general chemistry courses. To address this knowledge gap, we have developed a bimodal activity to teach students about the chemistry of climate change. Awareness of the impacts of light–matter interactions, properties that determine albedo and reflectivity, and properties of gases are crucial to understanding climate change. The program described herein shows promise in educating students on these topics. This program utilizes TED-Ed videos, simple demonstrations, and hands-on activities that can be reproduced in a classroom setting to supplement chemistry courses or in public outreach events.
Air quality is of great importance for human health and life expectancy. It becomes crucial to monitor atmospheric dust in the air of cities. In connection with the development of mobile networks and low-cost sensory agents, it has become possible to create inexpensive environmental monitoring systems. The paper presents results of studies on the system monitoring dust concentration in city air. The system consists of moving IoT agents placed on vehicles (taxies, busses, private cars) and measure the dust concentration. Agents, using a wireless connection, are sending the data to the recording server. The server application collects the data and visualises them on the map in a certain colour, depending on the dust concentration in the air and the values acceptable by standards. The system architecture, the algorithm of measurements and the agent-server data exchange protocol were presented in the article, as well as the example of data visualisation.
Air quality varies greatly in space and time across urban locales. However, criteria pollutants are typically monitored routinely at a relatively small number of surface sites within each metropolitan area, and routine vertical profiles of pollution are typically unavailable. We illustrate that a news helicopter provides an effective sensor platform to provide spatiotemporal analyses and vertical profiles of pollutant concentrations. We are unaware of any other air quality study that has utilized routine helicopter flights, despite the ubiquity of helicopters in urban environments across the world. Particulate and ozone concentration profiles have been collected since 2015 from sensors installed on a news helicopter that travels primarily over the metropolitan areas of northern Utah. The air quality data are retrieved in real time, archived, combined with surface-based observations, and disseminated in terms of time series and maps on a website for research, forecasting, and public awareness. Large vertical variations in particulate pollution concentrations were observed during the 2015-2016 winter associated with meteorological cold-air pool episodes. During the 2015 and 2016 summer seasons, ozone concentrations frequently exhibited complex spatial and temporal variations arising from many interrelated factors, including local terrain-forced circulations, lake breezes, and distant wildfires. © 2017 Turkish National Committee for Air Pollution Research and Control.
This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent methods and operated in a network of sites by citizen scientists (community members) in Houston, Texas and Denver, Colorado, under the umbrella of the NASA-led DISCOVER-AQ Earth Venture Mission. Measurements were focused on ozone (O3) and nitrogen dioxide (NO2). The performance evaluation showed that the CairClip O3/NO2 sensor provided a consistent measurement response to that of reference monitors (r² = 0.79 in Houston; r² = 0.72 in Denver) whereas the CairClip NO2 sensor measurements showed no agreement to reference measurements. The CairClip O3/NO2 sensor data from the citizen science sites compared favorably to measurements at nearby reference monitoring sites. This study provides important information on data quality from low-cost sensor technologies and is one of few studies that reports sensor data collected directly by citizen scientists.
Although air quality monitoring networks have been greatly improved, interpreting their expanding data in both simple and efficient ways remains challenging. Therefore, needed are new analytical methods. We developed such a method based on the comparison of pollutant concentrations between target and circum areas (circum comparison for short), and tested its applications by assessing the air pollution in Jing-Jin-Ji, Yangtze River Delta, Pearl River Delta and Cheng-Yu, China during 2015. We found the circum comparison can instantly judge whether a city is a pollution permeation donor or a pollution permeation receptor by a Pollution Permeation Index (PPI). Furthermore, a PPI-related estimated concentration (original concentration plus halved average concentration difference) can be used to identify some overestimations and underestimations. Besides, it can help explain pollution process (e.g., Beijing’s PM2.5 maybe largely promoted by non-local SO2) though not aiming at it. Moreover, it is applicable to any region, easy-to-handle, and able to boost more new analytical methods. These advantages, despite its disadvantages in considering the whole process jointly influenced by complex physical and chemical factors, demonstrate that the PPI based circum comparison can be efficiently used in assessing air pollution by yielding instructive results, without the absolute need for complex operations.
A sequential measurement method is demonstrated for quantifying the variability in exposure concentration during public transportation. This method was applied in Hong Kong by measuring PM2.5 and CO concentrations along a route connecting 13 transportation-related microenvironments within three-to-four hours. The study design takes into account ventilation, proximity to local sources, area-wide air quality and meteorological conditions. Portable instruments were compacted into a backpack to facilitate measurement under crowded transportation conditions and to quantify personal exposure by sampling at nose level. The route included stops next to three roadside monitors to enable comparison of fixed site and exposure concentrations. PM2.5 exposure concentrations were correlated with the roadside monitors, despite differences in averaging time, detection method and sampling location. Although highly correlated in temporal trend, PM2.5 concentrations vary significantly among microenvironments, with mean concentration ratios versus roadside monitor ranging from 0.5 for MTR train to 1.3 for bus terminal. Measured inter-run variability provides insight regarding the sample size needed to discriminate between microenvironments with increased statistical significance. The study results illustrate the utility of sequential measurement of microenvironments and policy-relevant insights for exposure mitigation and management.
Real time information become an usual way for common citizen to access and use data coming from own information systems. This imply new issues for ICT application in main fields and domains such as ‘energy efficiency’, sustainability, energy management. According to the growing interest in energy saving as a relevant component of territorial sustainability we developed an application based on open-source technologies providing real-time open data of energy consumptions. This hw-sw system can be oriented to individual householder needs, such as to industrial purposes and public ones. The paper discuss the preliminary results of the application of such technologies on public schools building in an integrated project linking usage model of public spaces to citizens behaviours and consciousness concerning sustainability. Outcomes could influence territorial policies and projects in the framework of EU 2020 strategy and Covenant of Majors.
Aiming at minimizing the costs, both of capital expenditure and maintenance, of an extensive air-quality measurement network, we present simple statistical methods that do not require extensive training datasets for automated real-time verification of the reliability of data delivered by a spatially-dense hybrid network of both low-cost and reference ozone measurement instruments. Ozone is a pollutant that has a relatively smooth spatial spread over a large scale although there can be significant small-scale variations. We take advantage of these characteristics and demonstrate detection of instrument calibration drift within a few days using a rolling 72 hour comparison of hourly-averaged data from the test instrument with that from suitably defined proxies. We define the required characteristics of the proxy measurements by working from a definition of the network purpose and specification, in this case reliable determination of the proportion of hourly averaged ozone measurements that are above a threshold in any given day, and detection of calibration drift of greater than ±30% in slope or ±5 parts-per-billion in offset. By analyzing results of a study of an extensive deployment of low-cost instruments in the Lower Fraser Valley, we demonstrate that proxies can be established using land-use criteria and that simple statistical comparisons can identify low-cost instruments that are not stable and therefore need replacing. We propose that a minimal set of compliant reference instruments can be used to verify the reliability of data from a much more extensive network of low-cost devices.
The collection of real-time air quality measurements while in motion (i.e.,
mobile monitoring) is currently conducted worldwide to evaluate in situ
emissions, local air quality trends, and air pollutant exposure. This
measurement strategy pushes the limits of traditional data analysis with
complex second-by-second multipollutant data varying as a function of time
and location. Data reduction and filtering techniques are often applied to
deduce trends, such as pollutant spatial gradients downwind of a highway.
However, rarely do mobile monitoring studies report the sensitivity of their
results to the chosen data-processing approaches. The study being reported
here utilized 40 h (> 140 000 observations) of mobile monitoring data
collected on a roadway network in central North Carolina to explore common
data-processing strategies including local emission plume detection,
background estimation, and averaging techniques for spatial trend analyses.
One-second time resolution measurements of ultrafine particles (UFPs), black
carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen
dioxide (NO2) were collected on 12 unique driving routes that were
each sampled repeatedly. The route with the highest number of repetitions was
used to compare local exhaust plume detection and averaging methods. Analyses
demonstrate that the multiple local exhaust plume detection strategies
reported produce generally similar results and that utilizing a median of
measurements taken within a specified route segment (as opposed to a mean)
may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce
similar but slightly lower estimates than a location-based method. For the
complete data set the estimated contributions of the background to the mean
pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%),
PM2.5-10 (45%), NO2 (57%), PM10 (60%), PM2.5
(68%). Lastly, while temporal smoothing (e.g., 5 s averages) results
in weak pair-wise correlation and the blurring of spatial trends, spatial
averaging (e.g., 10 m) is demonstrated to increase correlation and refine
spatial trends.
Health effects attributed to ambient fine particulate
matter (PM2.5) now rank it among the risk factors with the highest
health burdens in the world, but existing monitoring infrastructure cannot
adequately characterize spatial and temporal variability in urban PM2.5
concentrations, nor in human population exposures. The development and
evaluation of more portable and affordable monitoring instruments based on
low-cost sensors may offer a means to supplement and extend existing
infrastructure, increasing the density and coverage of empirical measurements
and thereby improving exposure science and control. Here, we report on field
calibrations of a custom-built, battery-operated aerosol monitoring
instrument we developed using low-cost, off-the-shelf optical aerosol
sensors. We calibrated our instruments using 1 h and 24 h PM2.5 data
from a class III US EPA Federal Equivalent Method (FEM) PM2.5
β-attenuation monitor in continuous operation at a regulatory
monitoring site in Oakland, California. We observed negligible associations
with ambient humidity and temperature; linear corrections were sufficient to
explain 60% of the variance in 1 h reference PM2.5 data and 72%
of the variance in 24 h data. Performance at 1 h integration times was
comparable to commercially available optical instruments costing considerably
more. These findings warrant further exploration of the circumstances under
which this class of aerosol sensors may profitably be deployed to generate
improved PM2.5 data sets.
Fine particulate matter (PM2.5) is a growing public health concern especially in industrializing countries but existing monitoring networks are unable to properly characterize human exposures due to low resolution spatiotemporal data. Low-cost portable monitors can supplement existing networks in both developed and industrializing regions to increase density of sites and data. This study tests the performance of a low-cost sensor in high concentration urban environments. Seven Portable University of Washington Particle (PUWP) monitors were calibrated with optical and gravimetric PM2.5 reference monitors in Xi'an, China in December 2013. Pairwise correlations between the raw PUWP and the reference monitors were high (R2 = 0.86–0.89). PUWP monitors were also simultaneously deployed at eight sites across Xi'an alongside gravimetric PM2.5 monitors (R2 = 0.53). The PUWP monitors were able to identify the High-technology Zone site as a potential PM2.5 hotspot with sustained high concentrations compared to the city average throughout the day.
The US Environmental Protection Agency (EPA) along with state, local, and tribal governments operate Federal Reference Method (FRM) and Federal Equivalent Method (FEM) instruments to assess compliance with US air pollution standards designed to protect human and ecosystem health. As the technological foundation of air pollution monitoring advances, new capabilities are being developed which can enhance our ability to determine ambient air pollutant concentrations. A new category of air pollution monitoring instruments called ‘sensors’ have emerged with a number of implications for the current US air monitoring strategy. Sensors have the potential to be used in compliance monitoring, however a number of considerations must be addressed. Fortunately EPA’s FEM Program, under the 40 CFR Part 53 regulations, provides a clear roadmap for upgrading air pollution monitoring devices and this guidance can be applied to sensors. The paper will discuss how new technology is integrated into EPA’s air monitoring program and how EPA’s regulations can be used to incorporate sensors into the US air monitoring network.
Daily commutes may contribute disproportionately to overall daily exposure to urban air pollutants such as particulate matter less than 2.5 μm (PM2.5) and carbon monoxide (CO). PM2.5 and CO concentrations were measured and compared across pedestrian, bus, and car modes during lunchtime and the afternoon peak hour within a 3-week time period on preselected round-trip routes. Variability in the concentration ratios of PM2.5 and CO for the selected transportation modes was quantified, and factors affecting variability in concentrations were identified. Exposure concentrations of transportation modes were sensitive to mode and were affected by factors such as vehicle ventilation and proximity to on-road emission sources. In general, pedestrian and bus modes had higher PM2.5 concentrations than did the car mode. However, the car mode had the highest average CO concentrations among the selected modes. Near-road pedestrian PM2.5 concentrations generally covaried with fixed site monitor (FSM) measurements, but there was little correlation between pedestrian CO concentrations and FSM data. Field studies such as this one are needed to develop input data for simulation models of population-based exposure to predict more accurately exposure concentrations for transportation modes.
Health effects attributed to ambient fine particulate matter (PM2.5) now rank it among the risk factors with the highest health burdens in the world, but existing monitoring infrastructure cannot adequately characterize spatial and temporal variability in urban PM2.5 concentrations, nor in human population exposures. The development and evaluation of more portable and affordable monitoring instruments based on low-cost sensors may offer a means to supplement and extend existing infrastructure, increasing the density and coverage of empirical measurements and thereby improving exposure science and control. Here, we report on field calibrations of a custom-built, battery-operated aerosol monitoring instrument we developed using low-cost, off-the-shelf optical aerosol sensors. We calibrated our instruments using 1 h and 24 h PM2.5 data from a class III US EPA Federal Equivalent Method (FEM) PM2.5 β-attenuation monitor in continuous operation at a regulatory monitoring site in Oakland, California. We observed negligible associations with ambient humidity and temperature; linear corrections were sufficient to explain 60% of the variance in 1 h reference PM2.5 data and 72% of the variance in 24 h data. Performance at 1 h integration times was comparable to commercially available optical instruments costing considerably more. These findings warrant further exploration of the circumstances under which this class of aerosol sensors may profitably be deployed to generate improved PM2.5 datasets.
The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data processing approaches. The study being reported here utilized a large mobile monitoring dataset collected on a roadway network in central North Carolina to explore common data processing strategies including time-alignment, short-term emissions event detection, background estimation, and averaging techniques. One-second time resolution measurements of ultrafine particles ≤ 100 nm in diameter (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) were collected on twelve unique driving routes that were repeatedly sampled. Analyses demonstrate that the multiple emissions event detection strategies reported produce generally similar results and that utilizing a median (as opposed to a mean) as a summary statistic may be sufficient to avoid bias in near-source spatial trends. Background levels of the pollutants are shown to vary with time, and the estimated contributions of the background to the mean pollutant concentrations were: BC (6%), PM2.5–10 (12%), UFPs (19%), CO (38%), PM10 (45%), NO2 (51%), PM2.5 (56%), and CO2 (86%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.
Excess air pollution along roadways is an issue of public health concern to Federal, State, and local government environmental agencies and the public. This concern was the motivation for a long-term study to measure levels of air pollutants at various distances from a roadway in Las Vegas, Nevada. This study represents a joint effort between the US Environmental Protection Agency and the US Department of Transportation’s Federal Highway Administration. Measurements of air pollutants—including carbon monoxide (CO), oxides of nitrogen (NO, NO2, NOX), and black carbon (BC)—were conducted concurrently at four distances from a major interstate (206,000 vehicles per day) for an entire year. With prevailing winds from the west, concentrations of all measured species at 20 m from the highway were significantly higher (non-overlapping 95% confidence intervals) than levels 300 m from the road. In addition, CO, NOX, and BC measured at 100 m from the road on the prevailing downwind side of the road were significantly higher than 100 m on the opposite side of the road. The disproportionate impact of the roadway emissions on the eastern side of the highway points to the importance of local meteorology in determining the extent of near-road impact. When isolating only time periods with winds from due west (±60°), CO, NO2, NOX, and BC levels at 20 m east of the highway were 60%, 46%, 122%, and 127% higher, respectively, than the concurrent measurements at the upwind site. Monthly average traffic volume and frequency of downwind conditions are not enough to explain the trends in monthly average excess CO at 20 m east of the road; average wind speed appears to be an important explanatory factor. The year-long extensive dataset afforded some unique data mining analyses—the maximum near-road impact (top 10% of 20 m east site minus 300 m east site) is associated with winds from the southwest to northwest, higher traffic volumes, and low wind speeds; meanwhile, the apparent maximum spatial extent in near-road impact (top 10% of 300 m east site minus to 100 m west site) occurred during evening to presunrise periods in the winter under conditions of low speed winds from due west, with moderate to low traffic volumes. This research confirms that excess air pollution associated with proximity to roads is significant over a year-long time frame and that local meteorology is a critical factor determining the extent of near-road impact.
Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.
Ultrafine particles (UFPs, diameter < 100 nm) and co-emitted pollutants from traffic are a potential health threat to nearby populations. During summertime in Raleigh, North Carolina, UFPs were simultaneously measured upwind and downwind of a major roadway using a spatial matrix of five portable industrial hygiene samplers (measuring total counts of 20–1000 nm particles). While the upper sampling range of the portable samplers extends past the defined “ultrafine” upper limit (100 nm), the 20–1000 nm number counts had high correlation (Pearson R = 0.7–0.9) with UFPs (10–70 nm) measured by a co-located research-grade analyzer and thus appear to be driven by the ultrafine range. Highest UFP concentrations were observed during weekday morning work commutes, with levels at 20 m downwind from the road nearly fivefold higher than at an upwind station. A strong downwind spatial gradient was observed, linearly approximated over the first 100 m as an 8% drop in UFP counts per 10 m distance. This result agreed well with UFP spatial gradients estimated from past studies (ranging 5–12% drop per 10 m). Linear regression of other vehicle-related air pollutants measured in near real-time (10-min averages) against UFPs yielded moderate to high correlation with benzene (R2 = 0.76), toluene (R2 = 0.49), carbon monoxide (R2 = 0.74), nitric oxide (R2 = 0.80), and black carbon (R2 = 0.65). Overall, these results support the notion that near-road levels of UFPs are heavily influenced by traffic emissions and correlate with other vehicle-produced pollutants, including certain air toxics.
Assessment of near-road air quality is challenging in urban environments that have roadside structures, elevated road sections, or depressed roads that may impact the dispersion of traffic emissions. Vehicles traveling on arterial roadways may also contribute to air pollution spatial variability in urban areas. To characterize the nature of near-road air quality in a complex urban environment, an instrumented all-electric vehicle was deployed to perform high spatial- and temporal-resolution mapping of ultrafine particles (UFPs, particle diameter <100 nm) and carbon monoxide (CO). Sampling was conducted in areas surrounding a highway in Durham, NC, with multiple repeats of the driving route accomplished within a morning or evening commute time frame. Six different near-road transects were driven, which included features such as noise barriers, vegetation, frontage roads, and densely built houses. Under downwind conditions, median UFP and CO levels in near-road areas located 20-150 m from the highway were a factor of 1.8 and 1.2 higher, respectively, than in areas characterized as urban background. Sampling in multiple near-road neighborhoods during downwind conditions revealed significant variability in absolute UFP and CO concentrations as well as in the rate of concentration attenuation with increasing distance from the highway. During low-speed meandering winds, regional UFP and CO concentrations nearly doubled relative to crosswind conditions; however, near-road UFP levels were still higher than urban background levels by a factor of 1.2, whereas near-road CO concentrations were not significantly different than the urban background.
The US Environmental Protection Agency recently conducted the Detroit Exposure and Aerosol Research Study (DEARS). The study began in 2004 and involved community, residential, and personal-based measurements of air pollutants targeting 120 participants and their residences. The primary goal of the study was to evaluate and describe the relationship between air toxics, particulate matter (PM), PM constituents, and PM from specific sources measured at a central site monitor with those from the residential and personal locations. The impact of regional, local (point and mobile), and personal sources on pollutant concentrations and the role of physical and human factors that might influence these concentrations were investigated. A combination of active and passive sampling methodologies were employed in the collection of PM mass, criteria gases, semivolatile organics, and volatile organic compound air pollutants among others. Monitoring was conducted in six selected neighborhoods along with one community site using a repeated measure design. Households from each of the selected communities were monitored for 5 consecutive days in the winter and again in the summer. Household, participant and a variety of other surveys were utilized to better understand human and household factors that might affect the impact of ambient-based pollution sources upon personal and residential locations. A randomized recruitment strategy was successful in enrolling nearly 140 participants over the course of the study. Over 36,000 daily-based environmental data points or records were ultimately collected. This paper fully describes the design of the DEARS and the approach used to implement this field monitoring study and reports select preliminary findings.
A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.
The air monitoring paradigm is rapidly changing due to advances in the development of portable, lower-cost air pollution sensors report high-time resolution data in near-real time along with supporting data and communication infrastructure. These changes are bringing forward opportunities to the traditional monitoring framework (supplementing ambient air monitoring and enhancing compliance monitoring) and also is expanding monitoring beyond this framework (personal exposure monitoring and community-based monitoring). Opportunities in each of these areas as well as corresponding challenges and potential solutions associated with development and implementation of air pollution sensors are discussed.
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