• NEANIAS Atmospheric Research Community

Advancements in the scientific understanding in the associated health implications of air pollution, coupled with raised public awareness and media coverage has brought this ever evolving public and environmental health issue into the forefront of society’s agenda, within the UK. The purpose of this study is to evaluate the impact the current Local Air Quality Management framework has had at reducing air pollution. The research project was designed in two phases: The initial structured literature search and review appraised the current body of knowledge in this area, developed an understanding of the known successes and shortfalls of Local Air Quality Management and highlighted areas were further understanding was required. The second phases built upon the established comprehension of Local Air Quality Management by apprising generalised theories against a local authority’s implementation, highlighting unseen influential factors and providing a quantitative analysis of empirical data to evaluate the impact this management system has had upon air pollution. Chartered Institute of Environmental Health University of Plymouth

Although State Implementation Plans (SIPs) typically rely on observations from ground-level monitoring networks and regulatory modeling, satellite data is increasingly available to state agencies. Below is an example of how one state agency used satellite data to supplement a state implementation plan to improve air quality. An advantage of satellite data is that it provides information for a broader area than sampled by ground-based networks. This document provides examples and guidance for using satellite products of nitrogen dioxide (NO2), a precursor to ground-level ozone and nitrate aerosol, in state implementation plans. It also provides some guidance on using SO2, a precursor to sulfate aerosol.

Compared to the sparse ground-based monitoring network, satellite observations have the advantage of coverage in unmonitored areas. This document describes a procedure for comparing tropospheric NO₂ columns simulated by the regional CMAQ model to those retrieved from the OMI satellite, with an example application in the Great Lakes Region. The tropospheric NO₂ profile shapes from CMAQ are used to derive new tropospheric vertical column densities (VCD) of NO₂ for comparison. The use of modeled NO₂ profile shapes ensures self-consistency and can improve retrieval accuracy through the improved spatial representation of the air mass factor that converts slant columns to vertical columns. The algorithm detailed in this document follows Goldberg et al. (2017). An implementation of the algorithm in R is appended to the document. Also included are instructions on how to download the data, how to use the algorithm to recalculate NO₂ VCD, how to regrid the data with WHIPS (the Wisconsin Horizontal Interpolation Program for Satellites), and how to perform the model-satellite data comparison.

Although State Implementation Plans (SIPs) typically rely on observations from ground-based networks and regulatory models, satellite data is increasingly available to state agencies and can also inform and supplement state implementation plans to improve air quality. An advantage of satellite data is that it provides information for a broader area than sampled by ground-based networks. This document provides examples and guidance for using satellite products of formaldehyde (HCHO) and nitrogen dioxide (NO2) to inform ground-level ozone sensitivity to emissions of nitrogen oxides (NOx) versus volatile organic compounds (VOC) in state implementation plans. Analysis of changes in ozone sensitivity over periods where emission controls have been implemented can provide insights into the efficacy of those past strategies and the likely efficacy of proposed future emission control programs.

Volatile organic compounds (VOCs) are a broad class of air pollutants which act as precursors to tropospheric ozone and secondary organic aerosols (a component part of PM2.5). The National Atmospheric Inventory (NAEI) indicates that UK emissions of anthropogenic VOCs peaked around 1990 at 2,840 kt yr-1 and then declined to ~810 kt yr-1 in 2017. Notable has been success in reducing emissions from the tailpipe of gasoline vehicles and other evaporative losses of VOCs from fuels (including natural gas) during their production and distribution. Ambient observations of selected VOCs in the Defra Automated Hydrocarbon Network also show significant declines since the 1990s, including species that are emitted directly from fuel loss, such as alkanes and mono-aromatics, and VOCs that are by-products of incomplete combustion such as alkenes and ethyne. The rates of reduction in ambient concentrations slowed around 2010 and have now plateaued. Benzene and 1,3 butadiene have specific limit and target values in the UK and concentrations of these have been successfully reduced such that the UK has reported no exceedances in recent years. Whilst both emissions and concentrations of VOCs have fallen, further reductions in VOC emissions are anticipated for the UK to meet obligations under the National Emission Ceiling Directive in 2030 and UNECE Convention on Long-Range Transport of Air Pollution.The relative contribution to UK emissions from solvents is estimated to have increased over the past 20 years, in 2017 representing ~74% of national emissions. Notable has been a post-2000 growth in emissions of oxygenated VOCs, none of which are routinely measured in regulatory networks. Ethanol is now the largest VOC emitted by mass (~136 kt yr-1 in 2017 or ~16.8% of total UK emissions) followed by n-butane (52.4 kt yr-1) and methanol (33.2 kt yr-1). Alcohols more generally have grown in significance, representing ~10% of VOC emissions in 1990 rising to ~30% in 2017. The growth in ethanol is due to increased reported emissions from the whisky industries and in estimated domestic use of ethanol as a solvent, for example contained within personal care, car care and household products. For some simple hydrocarbons there have also been notable changes in the major contributing sources. N-butane for example is currently the second most abundant VOC in the UK inventory; in 1990 n-butane was emitted overwhelmingly from gasoline extraction and fugitive distribution losses (139.8 kt yr-1). In 2017 the largest anthropogenic source of n-butane in the inventory was from its use as a domestic aerosol propellant (25.5 kt yr-1), with the gasoline/fugitive losses having been reduced to 23.3 kt yr-1.Recent changes in the contributing VOC sources to emissions then impacts on the observational strategies to verify emission reduction policies. In 1992 UK national monitoring in the Defra Automated Hydrocarbon Network quantified 19/20 of the most abundant anthropogenic VOCs emitted (all non-methane hydrocarbons), but by 2017 monitoring captured only 13/20 species. To evaluate progress across Europe towards meeting the future VOC emissions targets requires a revision of ambient monitoring strategies. Adding ethanol, methanol, formaldehyde, acetone, 2-butanone and 2-propanol to existing non-methane hydrocarbon measurements would provide full coverage of the 20 most significant VOCs emitted on an annual mass basis in the UK.

Satellite observations can provide useful information on long term changes in air quality. One of the most widely used satellite products is tropospheric NO₂, which has been retrieved from several different satellite sensors. We provide here a quick tutorial on using Giovanni to analyze a trend in tropospheric NO₂ data, from the NASA Ozone Monitoring Instrument (OMI) sensor, over a region of interest.

-Aerosol Optical Depth (AOD) data sets are used from satellite instruments MODIS Terra and Aqua and VIIRS which included a combination of the Dark Tark and Deep Blue algorithms and AOD from the NASA AERONET Network. -Surface PM2.5 data sets are from the State and Local Monitoring Station and Interagency Monitoring of Protected Visual Environments Networks. -PM2.5 model based data sets are from 3 separate chemical transport models; GEOS-Chem, WRF-Chem, and WRF-CMAQ. The EPA WRF-CMAQ data set is publicly available via the U.S. EPA Remote Sensing Information Gateway application. For CMAQ data access, users must first download and install the RSIG application at: https://www.epa.gov/hesc/remote-sensing-information-gateway.

The European Ambient Air Quality Directives (AAQD) have the overall objective to protect human health and the environment from ambient air pollution. This report provides an overview of air quality plans and measures reported for areas where the standards of air quality specified by the AAQD were not attained. The results are based on the reported data from 2014 to November 2020 by 23 countries to the EEA. The submitted data were analysed with the aim to provide information to the EEA Member countries that can be used to improve their air quality management practices, and to give feedback on data quality and possible use. Previous studies in the framework of the Air Implementation Pilot (published in 2012 and 2013) made assessments of the measures and management practices but were not successful in defining the measures��� effectiveness, so the present report also looks into what kind of information can be obtained from the data. In the period 2014 ��� 2020, 23 EEA member countries submitted at least one air quality plan. Most countries focus their plans on pollutants related to traffic: NO2 and/or PM10. Most measures target exceedances of NO2 (62 %), PM10 (26 %) and PM2.5 (10 %), and measures are reported that target exceedances of standards of benzo(a)pyrene, nickel and lead (all in PM10) as well as SO2. In one case, the measure is related to benzene. �� Traffic �� is the main sector leading to exceedances, with 64 % of records, followed by �� domestic heating �� (14 %), �� local industry �� (10 %) and ���Other��� (8 %). The �� Other �� category when given further information could comprise a variety of sources including meteorology, agricultural residue burning, harbour activity or shipping. The majority of the exceedances occurred in urban areas (65 %) followed by suburban areas (21 %), while 14 % of the exceedances addressed in the plans occurred in rural areas. The available data consist of a large number of individual records (ranging from several hundreds to over 20 thousand depending on the reporting element) that in theory can be linked using unique identifiers. However, not all the records can be linked. While the basis for analysis can vary depending on which reporting segments are used, the overall results are consistent across the segments, and provide a very good overview of which air pollution abatement measures are taken by national and local authorities. OCP/EEA/ACC/18/001-ETC/ATNI

Urban public buses worldwide carry hundreds of millions of passengers, all of whom inhale atmospheric pollutants in the form of particles and gases during what is commonly a daily commute. These urban journeys can provide a disproportionate percentage of the individual’s average daily exposure to inhalable contaminants, and recognition of this fact is reflected in a large body of scientific literature on air pollution and city transport that goes back over 50 years (e.g. Haagen-Smit, 1966; Fernández-Iriarte et al., 2020). The number of such publications has increased rapidly in recent years, accompanying the recognition of the damage to human health being inflicted by inhaling polluted urban air whilst travelling by road (e.g. Alameddine et al., 2016; Cepeda et al., 2017; Fruin et al., 2011; Hudda et al., 2011, 2012; Hudda and Fruin 2018; Jo and Yu, 2001; Leavey et al., 2017; Lee et al., 2015; Madl et al., 2015; Moreno et al., 2015, 2020; Tartakovsky et al., 2013; Xing et al., 2018; Yang et al., 2015; Zhu et al., 2007). A subset of publications dealing with air quality associated with transport microenvironments has included information specifically on buses and bus stops (e.g. Adams et al 2001; Asmi et al., 2009; Bel and Holst 2018; Chernyshev et al., 2018; Choi et al., 2018; Dales et al., 2007; Dons et a., 2012; Fernández- Iriarte 2020, 2021; Gajewski 2013; Hess et al., 2010; Li et al., 2009; Lim et al., 2015; Merritt et al., 2019; Moore et al., 2012; Moreno et al., 2015, 2020, 2021; Nogueira et al., 2019; Rivas et al., 2017; Schimek et al., 2001; Van Ryswyk et al., 2020; Velasco and Tan 2016; Wang et al., 2011). Most of these studies, whilst all providing valuable data, are based on relatively short sampling campaigns or models and commonly focused on a limited number of specific contaminants such as PM10 or PM2.5 mass, number concentrations of ultrafine particles (UFP), and/or levels of gaseous pollutants. The ambition of the Barcelona BUSAIR project was to provide a more integrated study by utilising a broad spectrum of monitoring instruments measuring particulate and gaseous pollutants simultaneously inside vehicles under normal weekday operating conditions across four seasons of the year. The sampling phase of the project took place from May 2017 to April 2018, backed up by detailed data on background city air conditions, and produced the largest freely available database on urban bus air quality to date. This Technical Guide overviews these data, identifies key influences on bus air quality, and draws conclusions and recommendations aimed to stimulate future reductions in public transport passenger exposure to urban pollutants. This work is supported by the Spanish Ministry of Economy, Industry and Competitiveness with FEDER funds (BUSAIR CGL2016-79132-R) and by CSIC with the “Proyecto Intramural: Exposición a compuestos organicos volatiles en el interior de autobuses de transporte publico urbano” (201730I003). Funding from the Generalitat de Catalunya (AGAUR 2017 SGR41) is also acknowledged. We thank J. Grimalt and E. Marco (IDAEA), and R. Pintò and A. Bosch (University of Barcelona) for their research collaboration in the publications on VOCs and SARS-CoV-2 respectively. We are grateful to Transports Metropolitans de Barcelona (TMB) for their co-operation throughout the whole project. The editor wishes to thank Wes Gibbons for his constant support and positive ideas. IDAEA-CSIC is a Centre of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S). Peer reviewed