Dataset Information

Combining sensor-based measurement and modeling of PM_2.5 and black carbon in assessing exposure to indoor aerosols.

ABSTRACT: Accurate, cost-effective methods are needed for rapid assessment of traffic-related air pollution (TRAP). Typically, real-time data of particulate matter (PM) from portable sensors have been adjusted using data from reference methods such as gravimetric measurement to improve accuracy. The objective of this study was to create a correction factor or linear regression model for the real-time measurements of the RTI's Micro Personal Exposure Monitor (MicroPEM^™) and AethLab's microAeth® black carbon (AE51) sensor to generate accurate real-time data for PM_2.5 (PM_2.5RT) and black carbon (BC_RT) in Cincinnati metropolitan homes. The two sensors and an SKC PM_2.5 Personal Modular impactor were collocated in 44 indoor sampling events for 2 days in residences near major roadways. The reference filter-based analyses conducted by a laboratory included particle mass (SKC PM_2.5 and MicroPEM^™ PM_2.5) and black carbon (SKC BC); these methods are more accurate than real-time sensors but are also more cumbersome and costly. For PM_2.5, the average correction factor, a ratio of gravimetric to real-time, for the MicroPEM^™ PM_2.5 and SKC PM_2.5 utilizing the PM_2.5RT and was 0.94 and 0.83, respectively, with a coefficient of variation (CV) of 84% and 52%, respectively; the corresponding linear regression model had a CV of 54% and 25%. For BC, the average correction factor utilizing the BC_RT and SKC BC was 0.74 with a CV of 36% with the associated linear regression model producing a CV of 56%. The results from this study will help ensure that the real-time exposure monitors are capable of detecting an estimated PM_2.5 after an appropriate statistical model is applied.

SUBMITTER: Cox J

PROVIDER: S-EPMC8320379 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Particulate matter (PM) is a major primary pollutant emitted during wildland fires that has the potential to pose significant health risks to individuals/communities who live and work in areas impacted by smoke events. Limiting exposure is the principle measure available to mitigate health impacts of smoke and therefore the accurate determination of ambient PM concentrations during wildland fire events is critical to protecting public health. However, monitoring air pollutants in smoke impacted environments has proven challenging in that measurement interferences or sampling conditions can result in both positive and negative artifacts. The EPA has performed research on methods for the measurement of PM2.5 in a series of laboratory-based studies including evaluation in smoke. This manuscript will summarize the results of the laboratory-based evaluation of federal equivalent method (FEM) monitors for PM2.5 with particular attention being given to the Teledyne-API Model T640 PM Mass monitor, as compared to the filter-based federal reference method (FRM). The T640 is an optical-based PM monitor and has been gaining wide use by state and local agencies in monitoring for PM2.5 U.S. National Ambient Air Quality Standards (NAAQS) attainment. At present, the T640 (includes both T640 and T640×) comprises ~44% of the PM2.5 FEM monitors in U.S. regulatory monitoring networks. In addition, the T640 has increasingly been employed for the higher time resolution comparison/evaluation of low-cost PM sensors including during smoke impacted events. Results from controlled non-smoke laboratory studies using generated ammonium sulfate aerosols demonstrated a generally negative T640 measurement artifact that was significantly related to the PM2.5 concentration and particle size distribution. Results from biomass burning chamber studies demonstrated positive and negative artifacts significantly associated with PM2.5 concentration and optical wavelength-dependent absorption properties of the smoke aerosol.Implications: The results detailed in this paper will provide state and local air monitoring agencies with the tools and knowledge to address PM2.5 measurement challenges in areas frequently impacted by wildland fire smoke. The observed large positive and negative artifacts in the T640 PM mass determination have the potential to result in false exceedances of the PM2.5 NAAQS or in the disqualification of monitoring data through an exceptional event designation. In addition, the observed artifacts in smoke impacted air will have a detrimental effect on providing reliable public information when wildfires occur and also in identifying reference measurements for small sensor evaluation studies. Other PM2.5 FEMs such as the BAM-1022 perform better in smoke and are comparable to the filter-based FRM. Care must be taken in choosing high time resolution FEM monitors that will be operated at smoke impacted sites. Accurate methods, such as the FRM and BAM-1022 will reduce the burden of developing and reviewing exceptional event request packages, data loss/disqualification, and provide states with tools to adequately evaluate public exposure risks and provide accurate public health messaging during wildfire/smoke events.

Project description:In Dhaka, Bangladesh, fine particulate matter (PM2.5) air pollution shows strong seasonal trends, with significantly higher mean concentrations during winter than during the monsoon (winter = 178.1 μg/m3 vs. monsoon = 30.2 μg/m3). Large-scale open burning of post-harvest agricultural waste across the Indo-Gangetic Plain is a major source of PM2.5 air pollution in northern India during the non-monsoon period. This study evaluates the extent to which the seasonal differences in PM2.5 pollution concentrations in Dhaka are accounted for by biomass-burning vs. fossil-fuel combustion sources. To assess this, an index was developed based on elemental potassium (K) as a marker for biomass particulate matter, after adjusting for soil-associated K contributions. Alternatively, particulate sulfur was employed as a tracer index for fossil-fuel combustion PM2.5. By simultaneously regressing total PM2.5 on S and adjusted K, the PM2.5 mass for each day was apportioned into: 1) fossil-fuels combustion associated PM2.5; 2) biomass-burning associated PM2.5; and, 3) all other PM2.5. The results indicated that fossil-fuel combustion contributed 21.6% (19.5 μg/m3), while biomass contributed 40.2% (36.3 μg/m3) of overall average PM2.5 from September 2013 to December 2017. However, the mean source contributions varied by season: PM2.5 in Dhaka during the monsoon season was dominated by fossil-fuels sources (44.3%), whereas PM2.5 mass was dominated by biomass-burning (41.4%) during the remainder of the year. The contribution to PM2.5 and each of its source components by transport of pollution into Dhaka during non-monsoon time was also evaluated by: 1) Conditional bivariate (CBPF) and pollution rose plots; 2) Concentration weighted trajectories (CWT), and; 3) NASA satellite photos to identify aerosol loading and fire locations on high pollution days. The collective evidence indicates that, while the air pollution in Dhaka is contributed to by both local and transboundary sources, the highest pollution days were dominated by biomass-related PM2.5, during periods of crop-burning in the Indo-Gangetic Plain.

Dataset Information

Combining sensor-based measurement and modeling of PM_2.5 and black carbon in assessing exposure to indoor aerosols.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Combining sensor-based measurement and modeling of PM2.5 and black carbon in assessing exposure to indoor aerosols.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Combining sensor-based measurement and modeling of PM_2.5 and black carbon in assessing exposure to indoor aerosols.