Impacts of Proximity to Primary Source Areas on Concentrations of POPs at Global Sampling Stations Estimated from...

Given the considerable financial and logistical resources supporting long-term monitoring for air pollutants, and the use of these data for performance evaluation of mitigation measures, it is important to account for contributions from primary versus secondary sources. We demonstrate a simple approach for using open source Global land cover raster data from the National Mapping Organization from the Geospatial Information Authority of Japan to assess local source inputs for air measurements of legacy persistent organic pollutants (POPs)-polychlorinated biphenyls (PCBs) and organochlorine pesticides-reported under the Global atmospheric passive sampling (GAPS) Network at 119 locations for the time period 2005-2014. The land cover composition within a 10 km radius around the GAPS sites was identified to create source impact indicator (SII) vectors to quantify and rank the remoteness of the sites from human infrastructure. Using principal component analysis, three SII vectors were established to rank sites by impact of (i) general infrastructure/remoteness, (ii) urban infrastructure, and (iii) agricultural infrastructure. General infrastructure describes the combined effects of settlements and agricultural infrastructure. We found significant correlations (p < 0.05) between POP concentrations in air and specific SIIs. PCB levels in air had a statistically significant correlation to the SII ranking urban impacts around the sampling sites, while Endosulfan I, Endosulfan II, and Endosulfan sulfate had a statistically significant correlation with SII ranking agricultural impacts. The complete GAPS data set from 2004-2014 (1040 samples at 119 locations) was standardized based on the SII rankings to assess the global temporal trends of legacy POPs. SIIs were incorporated in the multiple regression analysis to determine global halving times. This includes short-term monitoring data from 79 locations that were previously excluded. Furthermore, the SII approach allows the integration of global monitoring data from different studies for broader global temporal trend analysis. This ability to link the results of independent and small-scale studies can enhance temporal trend analysis in support of the larger scale initiatives, such as inter alia, the Global Monitoring Plan and Effectiveness Evaluation of the Stockholm Convention in the case of POPs. This simple approach using open source data has a broad potential for application for other classes of air pollutants.