The national ambient air monitoring strategy: rethinking the role of national networks

Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges

Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs.

Nonuniform impacts of COVID-19 lockdown on air quality over the United States

Most of the state governments in United States (U.S.) issued lockdown or business restrictions amid the COVID-19 pandemic in March 2020, which created a unique opportunity to evaluate the air quality response to reduced economic activities. Data acquired from 28 long-term air quality stations across the U.S. revealed widespread but nonuniform reductions of nitrogen dioxide (NO₂) and carbon monoxide (CO) during the first phase of lockdown (March 15-April 25, 2020) relative to a pre-lockdown reference period and historical baselines established in 2017-2019. The reductions, up to 49% for NO₂ and 37% for CO, are statistically significant at two thirds of the sites and tend to increase with local population density. Significant reductions of particulate matter (PM_2.5 and PM₁₀) only occurred in the Northeast and California/Nevada metropolises where NO₂ declined the most, while the changes in ozone (O₃) were mixed and relatively minor. These findings are consistent with lower transportation and utility demands that dominate NO₂ and CO emissions, especially in major urban areas, due to the lockdown. This study provides an insight into potential public health benefits with more aggressive air quality management, which should be factored into strategies to reopen the U.S. and global economy.

Air quality measurements-From rubber bands to tapping the rainbow

It is axiomatic that good measurements are integral to good public policy for environmental protection. The generalized term for "measurements" includes sampling and quantitation, data integrity, documentation, network design, sponsorship, operations, archiving, and accessing for applications. Each of these components has evolved and advanced over the last 200 years as knowledge of atmospheric chemistry and physics has matured. Air quality was first detected by what people could see and smell in contaminated air. Gaseous pollutants were found to react with certain materials or chemicals, changing the color of dissolved reagents such that their light absorption at selected wavelengths could be related to both the pollutant chemistry and its concentration. Airborne particles have challenged the development of a variety of sensory devices and laboratory assays for characterization of their enormous range of physical and chemical properties. Advanced electronics made possible the sampling, concentration, and detection of gases and particles, both in situ and in laboratory analysis of collected samples. Accurate and precise measurements by these methods have made possible advanced air quality management practices that led to decreasing concentrations over time. New technologies are leading to smaller and cheaper measurement systems that can further expand and enhance current air pollution monitoring networks.

IMPLICATIONS

Ambient air quality measurement systems have a large influence on air quality management by determining compliance, tracking trends, elucidating pollutant transport and transformation, and relating concentrations to adverse effects. These systems consist of more than just instrumentation, and involve extensive support efforts for siting, maintenance, calibration, auditing, data validation, data management and access, and data interpretation. These requirements have largely been attained for criteria pollutants regulated by National Ambient Air Quality Standards, but they are rarely attained for nonroutine measurements and research studies.

U.S. national PM2.5 Chemical Speciation Monitoring Networks-CSN and IMPROVE: description of networks

The US. EnvironmentalProtection Agency (EPA) initiated the national PM2.5 Chemical 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 PM2.5 using 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 PM2zs. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences infield and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN. Implications: Two 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 20000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researches 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.

A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O3) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs' objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O3 was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O3, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O3 monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions.

IMPLICATIONS

The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O3 and PM10 monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.

Spatial and seasonal distribution of aerosol chemical components in New York City: (1) Incineration, coal combustion, and biomass burning

We describe spatial and temporal patterns of fine particulate matter (PM(2.5)) and of 12 of its constituent chemical elements commonly observed in measurements at residential locations in New York City (NYC). These elements, that is, Ni, V, As, Se, S, Cl, Na, K, Pb, Cu, Zn, and Mn, had significant spatial and temporal variability at 10 PM(2.5) sampling locations during our winter and summer sampling campaigns. By grouping the elements into traditional source apportionment categories, we show that specific chemical components of PM(2.5) considered to have a common source category, such as As and Se for coal combustion, do not always follow the same temporal or spatial pattern. PM(2.5) mass had only limited spatial variability and a slight summertime concentration enhancement. Measurements at residential locations were, on average, consistent with EPA sampling network measurements, although we found that during times of low regional concentration, EPA measurements underestimated the PM(2.5) concentration at residential locations. These results have implications for improved understanding of exposures to specific sources of PM(2.5), and raise some concerns about source profiles used in source-receptor modeling tracer input selection.

Filter light attenuation as a surrogate for elemental carbon

Light attenuation (b(att)) measured from filter light transmission is compared with elemental carbon (EC) measurements for more than 180,000 collocated PM2.5 (particulate matter [PM] < or = 2.5 microm in aerodynamic diameter) and PM10 (PM < or = 10 microm in aerodynamic diameter) samples from nearly 200 U.S. locations during the past 2 decades. Although there are theoretical reasons for expecting highly variable relationships between b(att) and EC (such as the effects of "brown carbon" and iron oxides in PM2.5), reasonable correlations are found. These correlations are not a strong function of season or location (e.g., rural vs. urban). Median EC concentrations can be predicted from filter transmittance measurements to within +/- 15-30%. Although EC predicted from b(att) shows larger uncertainties (30-60%), especially at concentrations less than 0.3 microg/m3, the consistent mass absorption efficiency (sigm(att)) derived from the regression analysis demonstrates the feasibility of using b(att) as a surrogate for EC. This study demonstrates that a constant factor of 0.1 g/m2 (equivalent to the 10 m2/g sigma(att) used in the Interagency Monitoring of Protected Visual Environments chemical extinction formula) can be used to estimate EC concentrations from b(att) through a Teflon-membrane filter sample. Greater accuracy is achieved with site-specific sigma(att) derived from a period with collocated EC measurements.