English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

An integral part of air quality management is knowledge of the impact ofpollutant sources on ambient concentrations of particulate matter (PM).There is also a growing desire to directly use source impact estimates inhealth studies; however, source impacts cannot be directly measured. Severallimitations are inherent in most source apportionment methods motivating thedevelopment of a novel hybrid approach that is used to estimate sourceimpacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM–RM method calculates adjustmentfactors to refine the CTM-estimated impact of sources at monitoring sitesusing pollutant species observations and the results of CTM sensitivityanalyses, though it does not directly generate spatial source impact fields.The CTM used here is the Community Multiscale Air Quality (CMAQ) model, andthe RM approach is based on the chemical mass balance (CMB) model. This workpresents a method that utilizes kriging to spatially interpolatesource-specific impact adjustment factors to generate revised CTM sourceimpact fields from the CTM–RM method results, and is applied for January2004 over the continental United States. The kriging step is evaluated usingdata withholding and by comparing results to data from alternative networks.Data withholding also provides an estimate of method uncertainty. Directlyapplied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybridadjustment factors at withheld observation sites had a correlationcoefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and anintercept of 0.14 ± 0.02. Refined source contributions reflect currentknowledge of PM emissions (e.g., significant differences in biomass burningimpact fields). Concentrations of 19 species and total PM2.5 masswere reconstructed for withheld observation sites using HYB and SHadjustment factors. The mean concentrations of total PM2.5 atwithheld observation sites were 11.7 (± 8.3), 16.3 (± 11), 8.59(± 4.7), and 9.2 (± 5.7) μg m−3 for theobservations, CTM, HYB, and SH predictions, respectively. Correlationsimproved for concentrations of major ions, including nitrate (CMAQ–DDM (decoupled direct method):0.404, SH: 0.449), ammonium (CMAQ–DDM: 0.454, SH: 0.492), and sulfate(CMAQ–DDM: 0.706, SH: 0.730). Errors in simulated concentrationsof metals were reduced considerably: 295 % (CMAQ–DDM) to 139 % (SH) forvanadium; and 1340 % (CMAQ–DDM) to 326 % (SH) for manganese. Errors in simulated concentrations of some metals are expected to remaingiven the uncertainties in source profiles. Species concentrations werereconstructed using SH results, and the error relative toobserved concentrations was greatly reduced as compared to CTM-simulatedconcentrations. Results demonstrate that the hybrid method along with aspatial extension can be used for large-scale, spatially resolved sourceapportionment studies where observational data are spatially and temporallylimited

Development of PM&lt;sub&gt;2.5&lt;/sub&gt; source impact spatial fields using a hybrid source apportionment air quality model

Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model