Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps.

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of "low cost air quality monitors" (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.

Dispersion modeling of particulate matter containing hexavalent chromium during high winds in southern Iraq.

The aim of this paper is to describe a scientific methodology (i.e., the combination of different well-established modeling techniques) and its application to a real case scenario of contaminated dust emissions in high winds. This scenario addresses potential air pollution problems at the water treatment plant (WTP) at Qarmat-Ali, Basra, Iraq, during 2003. Workplace practices at the WTP before 2003 resulted in sodium dichromate contamination in the area. Looting at the site in early 2003 also contributed to this contamination. Individuals who were assigned to provide security at the site in 2003 have claimed adverse health effects caused by exposure to dust containing hexavalent chromium [Cr(VI)]. This report presents our modeling study with respect to these claims in relation to (1) amount of Cr(VI) present in the soil, (2) wind erosion episodes, and (3) possible long-term (e.g., annual average) Cr(VI) concentrations inhaled by different people while at the site. Our modeling approach included (1) the analysis of Cr(VI) soil measurements to assess the degree of contamination in different areas of the plant at different times; (2) the use of DUSTRAN model equations to calculate the emission rate of particulate matter (PM) less than 10 µm in diameter (PM10) during high-wind episodes; (3) the use of the U.S. Environmental Protection Agency (EPA) AERMOD modeling system to estimate Cr(VI) concentrations at the site; and (4) the calculation of modeling results in the form of both contour lines of average Cr(VI) concentrations at the site, and specific concentration values for selected individuals, based upon their recollection of their visits to the site.