Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

摘要

Medium-term air quality assessment,benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes.By using daily and monthly averaged data,medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives.By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark(CLAB).The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator.Such a benchmark can provide an overall air quality assessment for areas of interest.A case study is presented for Dallas County(U.S.A.)applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019.Six air pollutants considered are:ozone,carbon monoxide,nitrogen dioxide,sulfur dioxide,benzene and particulate matter less than 2.5 micrometres.These pollutants are assessed individually and in terms of CLAB,and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019.Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years.The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs.Conducting CLAB analysis on an ongoing basis,using a relevant near-past time interval for benchmarking that covers several years,can reveal useful monthly,seasonal and annual trends in overall air quality.This type of medium-term,benchmarked air quality data analysis is well suited for ecosystem monitoring.