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Air pollution monitoring using mobile ground-based measurement platforms can provide high-quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller-scale air quality sensors, it is important to address the need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ± 4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases. [ABSTRACT FROM AUTHOR]

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the quality assurance needs for validating these measurements. We explore collocation-based evaluation of air quality instruments in a mobile platform against fixed regulatory sites, both when the mobile platform is parked at the fixed regulatory site and when moving at distances of meters to kilometers from the site. We demonstrate agreement within 4 ppbv (for NO2 and O3+NO2) to 10 ppbv (for NO) when using a running median of 40 hourly differences between the moving mobile platform measurements and stationary site measurements. The comparability is strong when only measurements from residential roads are used but are only slightly diminished when all roads except highways are included in the analysis. We present a method for assessing mobile measurements of ozone (O3), nitrogen dioxide (NO2), and odd oxygen (OX = O3 + NO2) on an ongoing basis through comparisons with fixed regulatory sites. [ABSTRACT FROM AUTHOR]

Mobile-platform measurements provide new opportunities for characterizing spatial variations in air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc., mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1 s) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including nonurban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations in air pollutant concentrations over measurement periods as short as 2 weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4), black carbon (BC), and particle number (PN) concentration, with bias and precision ranging from < 10 % for gases to < 25 % for BC and PN at 1 s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that United States Environmental Protection Agency (US EPA) classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by < 1 to 10 km. A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1 km scale differences in mean NO2 and O3 concentrations up to 117 % and 46 %, respectively, of mean values during a 2-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100–300 m, corresponding to 1 min averages. [ABSTRACT FROM AUTHOR]

The U.S. Environmental Protection Agency (EPA) initiated the national PM2.5Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM2.5using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described. [ABSTRACT FROM AUTHOR]

The article considers the Particulate Matter Supersites Program air quality monitoring program of the U.S. Environmental Protection Agency (EPA). Results obtained from the Supersites Program and their application to air quality policy development by the EPA, state and local government agencies and private business enterprises are discussed.

Abstract: We present the design and fabrication of a micro electro mechanical systems (MEMS) air-microfluidic particulate matter (PM) sensor, and show experimental results obtained from exposing the sensor to concentrations of tobacco smoke and diesel exhaust, two commonly occurring PM sources. Our sensor measures only 25mm×21mm×2mm in size and is two orders of magnitude smaller than commercially available direct mass PM sensors. The small shape allows our sensor to be used for continuous recording of personal PM exposure levels. The sensor contains an air-microfluidic circuit that separates the particles by size (virtual impactor) and then transports and deposits the selected particles using thermophoretic precipitation onto the surface of a microfabricated mass-sensitive film bulk acoustic resonator (FBAR). The mass-loading of the FBAR causes a change in its resonant frequency, and the rate of the frequency change corresponds to the particle concentration in the sampled air volume. We present experimental results that demonstrate the performance of our sensor for measuring PM mass emitted from diesel exhaust and tobacco smoke, and show that it exhibits sensitivity approaching 2μg/m3 with up to 10min integration time. [Copyright &y& Elsevier]