Ambient air quality measurements from a continuously moving mobile platform: Estimation of area-wide, fuel-based, mobile source emission factors using absolute principal component scores

This article reports on an interdisciplinary evaluation of the pilot phase of a community-driven civic science project. The project investigates the distribution of heavy metals in air pollution using moss growing on street trees as a bio-indicator in two industrial-adjacent neighborhoods in Seattle, Washington (USA). One goal of the ongoing project is to meaningfully engage local urban youths (eighth to twelfth grade) in the scientific process as civic scientists, and teach them about environmental health, environmental justice, and urban forestry concepts in a place-based, urban-oriented environmental research project. We describe the collaborative context in which our project developed, evaluate the quality of youth-collected data through analysis of replicate samples, and assess participants’ learning, career interests, and overall appraisal of the pilot. Our results indicate that youth scientists collected usable samples (with acceptable precision among repeated samples), learned project content (with statistically significant increases in scores of test-style survey questions; p = 0.002), and appraised their engagement favorably (with 69% of participants reporting they liked the project). We observed few changes in career interests, however. We discuss our intention to use these preliminary insights to further our community-driven education, research, and action model to address environmental injustices.

Section: Methodsmentioning

confidence: 99%

Toward Environmental Justice in Civic Science: Youth Performance and Experience Measuring Air Pollution Using Moss as a Bio-Indicator in Industrial-Adjacent Neighborhoods

Derrien

Zuidema

Jovan

et al. 2020

“…The purpose of the algorithm is to estimate a smooth curve that represents how the minimum of the concentrations varies over time. The algorithm proposed in Brantley et al [14] involved calculating the minimum values within a given window size (e.g., 10 min) and fitting an ordinary least squares regression spline through the minimums. In this paper, we simplify and improve the algorithm by using a natural spline basis expansion of time with quantile regression rather than choosing a window size and calculating minimums.…”

Section: Methodsmentioning

confidence: 99%

“…Mobile monitoring, which is the approach used in this present study, provides the ability to conduct observations over a large spatial area and to correlate emission measurements with sources. Therefore, mobile monitoring is becoming an increasingly common strategy of studying air quality and emission sources (e.g., [12,13,14]); however, this approach of measurement is nontrivial in its labor-intensive method of collecting data, and requires both high time-resolution measurement approaches and advanced data analysis methods to process and interpret the results [15].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Spatial Air Pollution Patterns Near a Large Railyard Area in Atlanta, Georgia

Brantley

Hagler

Herndon

et al. 2019

Railyards are important transportation hubs, and they are often situated near populated areas with high co-located density of manufacturing, freight movement and commercial enterprises. Emissions occurring within railyards can affect nearby air quality. To better understand the air pollution levels in proximity to a major railyard, an intensive mobile air monitoring study was conducted in May 2012 around a major railyard area in Atlanta, GA, constituted of two separate facilities situated side-by-side. A total of 19 multi-hour mobile monitoring sessions took place over different times of day, days of the week, and under a variety of wind conditions. High time resolution measurements included black carbon (BC), particle number concentration (PN), particle optical extinction (EXT), oxides of nitrogen (NO, NO2, NOy), carbon monoxide (CO), and speciated air toxics. Urban background was estimated to contribute substantially (>70%) to EXT and CO, whereas BC, oxides of nitrogen (NOx) and toluene had comparably low background contributions (<30%). Mobile monitoring data were aggregated into 50 meter spatial medians by wind categories, with categories including low speed wind conditions (<0.5 m s−1) and, for wind speeds above that threshold, by wind direction relative to the railyard. Spatial medians of different pollutants measured had a wide range of correlation—gas-phase air toxics (benzene, toluene, acetaldehyde) had moderate correlation with each other (r = 0.46–0.59) and between toluene and CO (r = 0.53), but lower correlation for other pairings. PN had highest correlation with oxides of nitrogen (r = 0.55–0.66), followed by BC (r = 0.4), and lower correlation with other pollutants. Multivariate regression analysis on the full set of 50 m medians found BC and NO as having the strongest relationship to railyard emissions, in comparison to their respective background levels. This was indicated by an increase associated with transiting through the yard and inverse relationship with distance from the railyard; NO and BC decreased by a factor of approximately 0.5 and 0.7 over 1 km distance of the railyard boundary, respectively. Low speed, variable wind conditions were related to higher concentrations of all measured parameters.

“…Numerical modeling requires a comprehensive understanding of the transportation and transformation mechanisms of the pollutants, as well as a large amount of data to support it [ 26 ]. In contrast, the PMF and PCA/FA-MLR models depend less on the source component spectrum and mainly use the variation of water quality parameters to analyze the potential pollution sources and their contributions [ 15 , 27 ]. However, the model requires researchers to judge the number of pollution sources and their types, which may cause bias in the pollution source analysis on account of the different perceptions of the researchers [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Pollution Source Apportionment and Water Quality Risk Evaluation of a Drinking Water Reservoir during Flood Seasons

Qin

Liu

et al. 2021

Reservoirs play an important role in the urban water supply, yet reservoirs receive an influx of large amounts of pollutants from the upper watershed during flood seasons, causing a decline in water quality and threatening the water supply. Identifying major pollution sources and assessing water quality risks are important for the environmental protection of reservoirs. In this paper, the principal component/factor analysis-multiple linear regression (PCA/FA-MLR) model and Bayesian networks (BNs) are integrated to identify water pollution sources and assess the water quality risk in different precipitation conditions, which provides an effective framework for water quality management during flood seasons. The deterioration of the water quality of rivers in the flood season is found to be the main reason for the deterioration in the reservoir water quality. The nonpoint source pollution is the major pollution source of the reservoir, which contributes 53.20%, 48.41%, 72.69%, and 68.06% of the total nitrogen (TN), phosphorus (TP), fecal coliforms (F.coli), and turbidity (TUB), respectively. The risk of the water quality parameters exceeding the surface water standard under different hydrological conditions is assessed. The results show that the probability of the exceedance rate of TN, TP, and F.coli increases from 91.13%, 3.40%, and 3.34%, to 95.75%, 25.77%, and 12.76% as the monthly rainfall increases from ≤68.25 mm to >190.18 mm. The risk to the water quality of the Biliuhe River reservoir is found to increase with the rising rainfall intensity, the water quality risk at the inlet during the flood season is found to be much greater than that at the dam site, and the increasing trend of TP and turbidity is greater than that of TN and F.coli. The risk of five-day biochemical oxygen demand (BOD5) does not increase with increasing precipitation, indicating that it is less affected by nonpoint source pollution. The results of this study can provide a research basis for water environment management during flood seasons.