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Air Quality Citizen Science Research Project in NYC: Toolkit & Case Studies
Abstract
Increase citizen engagement in accessing, collecting, and communicating air quality data, thus providing tools to better inform communities on air quality issues. Provide communities with information for advocating for clean air. Increase data collection in communities that can offer additional spatial and temporal da-ta on pollution levels beyond existing NYCCAS and regulatory methods. These data can offer valuable insights into gradients near major sources and temporal characteristics that contribute to chronically high levels of pollution in many neighborhoods. Produce data for research efforts aimed at combining data from low-cost sensor networks with data from existing NYCCAS or regulatory monitoring networks. These statistical fusion techniques can help develop more spatiotemporally resolved exposure maps of air pollution exposure and inform how the City and other researchers use sensor data in the future. Develop data systems that allow for remote uploading of data to servers or citizen uploading of air quality data.
... accessed on 15 February 2022), which was informed and shaped by the work performed within the STAR Grant. This toolkit is intended to complement currently available resources, such as the Citizen Science Toolbox offered by the U.S. EPA [44] and previously developed air sensor guidebooks [45][46][47]. The resources in the toolkit have the advantage of leveraging the results of a large-scale (nearly 400 sensor units deployed and over 350 community members engaged), long-term (with sensor deployments lasting up to three years) project conducted with 14 different California communities. ...
As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users’ experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled “Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors”, approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled “Community in Action: A Comprehensive Toolkit on Air Quality Sensors”. This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health.
This study focuses on soil methane and radon gas measurements, in the subsurface as well as in the atmosphere. Measurements were conducted using new gas detection instrumentation and as low-cost devices for methane gas concentrations. Moreover, this study focuses on a better understanding of the advantages and disadvantages of new soil gas detection technology. The results and findings of environmental data obtained from the soil gas survey were shared with the community whose involvement was critical in data acquisition process.
There is a growing field of ‘citizen scientists’, non-scientists engaged in specific issues who collect or analyze data to contribute to scientific research or advocate for environmental or public health improvements. Specific aims of this study included increase citizen engagement in accessing, collecting, and communicating air quality data, thus providing tools to better inform communities on air quality issues and increased data collection in communities that can offer additional spatial and temporal data on pollution levels beyond existing New York City Community Air Survey (NYCCAS) program and regulatory methods in the New York City.
This Air Quality Citizen Science research project aims to provide a better awareness and understanding of local hotspots of fine particulate matter (PM2.5) concentrations in the Williamsburg, Brooklyn neighborhood, which is prone to a high rate of asthma and cardio-respiratory diseases. A key component of the project is to involve the local population in all aspects of the study, ranging from project design to implementation. Members of the community and high school students participated in both defining the study objectives and the collection of air quality data using low-cost sensor technology (AirBeam2) on the basis of personal monitoring procedures. Key targets for the data collection on PM2.5 exposure included schools and playgrounds near major roadways. Project participants attended workshops and training sessions to better understand air pollution in their community and to learn how to use low cost sensor technology to collect and analyze environmental data. In addition to personal monitoring activities, a fixed-site monitoring network, using low-cost Airbeam2 devices, was set up at 12 locations in the Williamsburg neighborhood, which provide real-time PM2.5 air concentrations that are transmitted to a cloud server. Tableau software is being used for data visualization and risk communication. Prior to the use of low cost sensor technology for personal monitoring and the fixed-site network in the project area, an assessment of the performance of all Airbeam2 instruments was performed under ambient conditions at the Queens College-based regulatory monitoring site. Preliminary data indicate distinct spatial patterns of PM2.5 concentrations in the project area of Williamsburg. Some of the questions which will be answered through this Citizen Science pilot study involve a better understanding of the effectiveness of community and volunteer collaboration and an assessment of the efficacy of low cost sensor technology to describe fine scale spatial-temporal characteristics of the project area in Williamsburg, Brooklyn.
Traditional approaches to air quality monitoring generally involve regulatory agencies that utilize expensive and complex stationary equipment maintained by trained staff to provide the type of highly accurate data needed for demonstrating attainment with federal air quality standards. While this type of monitoring is a vital component to air quality management, in urban areas these monitors are often deployed at a limited number of rooftop locations. They are intended to track urban scale trends in pollution levels and are not spatially dense enough to characterize intra-urban spatial variation in air quality due to local emissions sources such as traffic. To address this limitation, in 2007 the NYCDOHMH in partnership with Queens College launched the New York City Community Air Survey, a high density monitoring network designed to assess spatial variation in longer term exposures (seasonal and annual average) at the neighborhood-level. NYCCAS uses less expensive monitoring technology than those that meet federal requirements for NAAQS-attainment determination (Federal Reference Methods), trading high temporal resolution achieved by more expensive monitoring methods with increased spatial coverage that can be achieved by deploying larger numbers or lower cost and easier to deploy instrumentation.
NYCCAS has become vital to the City’s understanding of the variation in pollution exposures within New York City; however, its operation relies on trained technical, analytic, and field staff to collect and analyze air quality data. In recent years, technological advancements in air quality monitoring have brought to market many lower-cost, easy-to-use, portable air quality sensors that provide high time resolution data in real time which provides exciting opportunities for additional data collection. Simultaneously, there is a growing field of ‘citizen scientists’, non-scientists who are engaged in specific issues that collect or analyze data to contribute to scientific research or advocate for environmental or public health improvements. The NYCCAS team is currently expanding into the area of community engagement and community-based participatory research by developing air quality “citizen-science” toolkits that will include how-to guides for accessing available data on emission sources, designing neighborhood air pollution surveys using new, low-cost technologies, and sharing data online. We will discuss the role of citizen science projects in metropolitan areas where a considerable understanding of local air pollution exposure already exists.
The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772
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.
This project explored the feasibility of using stationary low-cost monitoring networks for spatial and temporal estimation of ambient fine particulate concentrations in two environmental justice communities in New York City-El Puente (Brooklyn) and Youth Ministries for Peace and Justice (Bronx). The data from the community-based low-cost stationary monitoring networks were compared to FEM/FRM data and the findings land use regression (LUR) analysis of the New York City Community Air Survey (NYCCAS).
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.
A recent study by the National Academy of Sciences indicated that man’s activities definitely have an impact on the face of the Earth. Ten percent of the entire land surface is used for cultivation to grow crops and 30 percent of the Earth’s land surface is under some form of use by man. Prior to the dawn of the industrial age when we substituted machines for human and animal power, our ability to influence our global neighbors was limited but at the time of the industrial revolution and at an accelerating pace since then, our activities can influence our neighbors thousands of kilometers away from us.