A Breath of Fresh Air

From national emission totals to regional ambient air quality information for Austria

INTRODUCTION: Imperial County, California, is a low-income, primarily Latino community that faces disproportionate impacts from a range of environmental hazards. With consistently high rates of emergency room visits and hospitalizations for asthma among schoolchildren, the community identified air pollution as a priority concern. Community members collaborated with researchers to build a community air monitoring network to provide local data on air quality that can be used to protect community health. METHODS: A community-based organization responsive to local concerns participated in research planning from the outset, and a Community Steering Committee guided the project. Community members selected priority neighborhoods within the county for air monitoring, and over 40 local residents identified vulnerable population groups. Key informant interviews informed data display decisions. Data was visualized using an environmental health web platform developed by the community. RESULTS: Community participation increased the usefulness of the monitoring network to the community. Community members identified schools throughout the county as priorities for siting the first 20 monitors. Local schools and businesses facilitated monitor deployment, promoting local capacity for maintenance. Training on air monitoring established a common knowledge base. Selection of monitor sites and hosting of monitors by local residents promoted community ownership of the monitoring network. Community input determined priority data display needs. Conclusions: Substantial engagement of and leadership by community members at every stage of planning and implementation of an air monitoring network (1) enhances responsiveness of research to community priorities, (2) fosters community ownership of the project and builds local capacity, (3) ensures accessibility, usability and usefulness of data displayed, and (4) promotes potential for network sustainability beyond the research period.

Fil: Mazzeo, Nicolas Antonio. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmosfera y los Oceanos; Argentina

Air pollution and its impact have become one of the most important challenge for public authorities. The quantification of emissions as well as their spatial distribution are essential for any air quality program (Aleksandropoulou & Lazaridis, 2004; Sengupta et al., 1996). The selection of the location of monitoring stations is one of the most complex task that occurs in designing air monitoring networks. Several issues, as the harmful effects of pollution on both human health and environment, must be taken into account (Allegrini et al., 2004). The European directive 2008/50/CE of 21 May 2008 on ambient air quality and cleaner air provides criteria about monitoring network. This directive has been issued in order to improve, clarify, simplify and replace the precedents five acts: • Council Directive 96/62/EC of 27 September 1996 on ambient air quality assessment and management; • Council Directive 1999/30/EC of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and nitrogen oxides, particulate matter and lead in ambient air; • Directive 2000/69/EC of the European Parliament and of the Council of 16 November 2000 relating to limit values for benzene and carbon monoxide in ambient air; • Directive 2002/3/EC of the European Parliament and of the Council of 12 February 2002 relating to ozone in ambient air and; • Council Decision 97/101/EC of 27 January 1997 establishing a reciprocal exchange of information and data from networks and individual stations measuring ambient air pollution within the Member States. The Directive 2008/50/CE also introduces new air quality objectives and monitoring requirements for PM2.5. In addition to that the EU directive determines criteria for positioning monitoring stations, taking into account a detailed evaluation of environmental features on both local and regional scale. These objectives can be pursued by territorial analysis, which can be performed using a Geographic Information System (GIS). GIS is a computer-based information system that enables storing, modelling, manipulation, retrieval, analysis and presentation of geographically referenced data (Burrough, 2001). In particular this powerful tool allows a

Design of air quality monitoring networks and its application to NO 2 and O 3 in Cordova, Spain

Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

A quantitative assessment of the Idaho National Laboratory (INL) air monitoring network was performed using frequency of detection as the performance metric. The INL air monitoring network consists of 37 low-volume air samplers in 31 different locations. Twenty of the samplers are located on INL (onsite) and 17 are located off INL (offsite). Detection frequencies were calculated using both BEA and ESER laboratory minimum detectable activity (MDA) levels. The CALPUFF Lagrangian puff dispersion model, coupled with 1 year of meteorological data, was used to calculate time-integrated concentrations at sampler locations for a 1-hour release of unit activity (1 Ci) for every hour of the year. The unit-activity time-integrated concentration (TICu) values were calculated at all samplers for releases from eight INL facilities. The TICu values were then scaled and integrated for a given release quantity and release duration. All facilities modeled a ground-level release emanating either from the center of the facility or at a point where significant emissions are possible. In addition to ground-level releases, three existing stacks at the Advanced Test Reactor Complex, Idaho Nuclear Technology and Engineering Center, and Material and Fuels Complex were also modeled. Meteorological data from the 35 stations comprising the INL Mesonet network, data from the Idaho Falls Regional airport, upper air data from the Boise airport, and three-dimensional gridded data from the weather research forecasting model were used for modeling. Three representative radionuclides identified as key radionuclides in INL’s annual National Emission Standards for Hazardous Air Pollutants evaluations were considered for the frequency of detection analysis: Cs-137 (beta-gamma emitter), Pu-239 (alpha emitter), and Sr-90 (beta emitter). Source-specific release quantities were calculated for each radionuclide, such that the maximum inhalation dose at any publicly accessible sampler or the National Emission Standards for Hazardous Air Pollutants maximum exposed individual location (i.e., Frenchman’s Cabin) was no more than 0.1 mrem yr–1 (i.e., 1% of the 10 mrem yr–1 standard). Detection frequencies were calculated separately for the onsite and offsite monitoring network. As expected, detection frequencies were generally less for the offsite sampling network compared to the onsite network. Overall, the monitoring network is very effective at detecting the potential releases of Cs-137 or Sr-90 from all sources/facilities using either the ESER or BEA MDAs. The network was less effective at detecting releases of Pu-239. Maximum detection frequencies for Pu-239 using ESER MDAs ranged from 27.4 to 100% for onsite samplers and 3 to 80% for offsite samplers. Using BEA MDAs, the maximum detection frequencies for Pu-239 ranged from 2.1 to 100% for onsite samplers and 0 to 5.9% for offsite samplers. The only release that was not detected by any of the samplers under any conditions was a release of Pu-239 from the Idaho Nuclear Technology and Engineering Center main stack (CPP-708). The methodology described in this report could be used to improve sampler placement and detection frequency, provided clear performance objectives are defined.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes. In a European legislative context, the data collected constitutes the basis for reporting air quality status and exceedances under the Ambient Air Quality Directives (AAQD) following specific requirements. Consequently, the network's representativity and ability to accurately assess the air pollution situation in European countries become a key issue. The combined use of models and measurements is currently understood as the most robust way to map the status of air pollution in an area, allowing it to quantify both the spatial and temporal distribution of pollution. This spatial-temporal information can be used to evaluate the representativeness of the monitoring network and support air quality monitoring design using hierarchical clustering techniques.The hierarchical clustering methodology applied in this context can be used as a screening tool to analyse the level of similarity or dissimilarity of the air concentration data (time-series) within a monitoring network. Hierarchical clustering assumes that the data contains a level of (dis)similarity and groups the station records based on the characteristics of the actual data. The advantage of this type of clustering is that it does not require an a priori assumption about how many clusters there might be, but it can become computationally expensive as the number of time-series increases in size. Three dissimilarity metrics are used to establish the level of similarity (or dissimilarity) of the different air quality measurements across the monitoring network: (1) 1-R, where R is the Pearson linear correlation coefficient, (2) the Euclidean distance (EuD), and (3) multiplication of metric (1) and (2). The metric based on correlation assesses dissimilarities associated with the changes in the temporal variations in concentration. The metric based on the EuD assesses dissimilarities based on the magnitude of the concentration over the period analysed. The multiplication of these two metrics (1-R) x EuD assesses time variation and pollution levels correlations, and it has been demonstrated to be the most useful metric for monitoring network optimization.This study presents the MoNET webtool developed based on the hierarchical clustering methodology. This webtool aims to provide an easy solution for member states to quality control the data reported as a tier-2 level check and evaluate the representativeness of the air quality network reporting under the AAQD. Some examples from the ongoing evaluation of the monitoring site classification carried out as a joint exercise under the Forum for Air Quality Modeling (FAIRMODE) and the National Air Quality Reference Laboratories Network (AQUILA) are available to show the usability of the tool. MoNet should be able to identify outliers, i.e., issues with the data or data series with very specific temporal-magnitude profiles, and to distinguish, e.g., pollution regimes within a country and if it resembles the air quality zones required by the AAQD and set by the member states; stations monitoring high-emitting sources; background regimes vs. a local source driving pollution regime in cities.

Within the framework of multiple international conventions, Germany like other state parties is committed to monitor the air quality in the atmospheric background. Therefore, atmospheric measurements are realized by the German Environment Agency (Umweltbundesamt - UBA) with its network of 7 remote measurement stations throughout the rural background of Germany. These stations are operated by personnel and contribute data on pollutant deposition and transboundary long-range transport to the following monitoring programs: Global Atmosphere Watch (GAW), European Monitoring and Evaluation Program (EMEP), Convention on the Protection of the Marine Environment of the Baltic Sea Area (HELCOM), Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) and as well as to the EU commission within the directive on ambient air quality and cleaner air for Europe (2008/50/EC).Some pollutants are measured continuously since the late 1960s, while other pollutants especially metals and semi-metals are monitored since the early 1990s. Organic pollutants such as PAHs and POPs are regularly monitored as well starting in the mid-1990s in precipitation and since the mid-2000s also in air and the aerosol phase. Therefore, the UBA air monitoring network contributes to the supervision of the Stockholm convention and the respective EU Regulation (2019/1021) on persistent organic pollutants.Recently further chemicals of emerging concern were included to the list of substances that are measured at the UBA air monitoring stations. Within a three-year project period, fluorinated organics such as per- and polyfluorinated substances (PFAS) and a range of current used pesticides (CUP) will be measured for the next two years in precipitation and air. This work presents the history of PAH and POP measurements at the UBA air monitoring network and the novel compounds of interest with the applied techniques for their detection and monitoring over the coming years.

This paper describes a statistical method to assess site redundancy of urban air monitoring networks in reporting daily Pollutant Standards Index (PSI), average concentrations, and the number of exceedances. Such a statistical method has identified significant redundancy in monitoring sites for one-year measurements of two air monitoring networks in Taiwan. There are five redundant sites out of 15 monitoring sites in the Taipei area and eight redundant sites out of 18 monitoring sites in the Kaohsiung area. By using the statistical method presented in this paper to downsize the monitoring networks, we can determine not only the number of redundant sites but also the priority of site removals. The derived sub-networks can maintain consistency in reporting air quality without significant changes in the spatial variations of air measurements for an existing air monitoring network.

An air monitoring network using continuous particle size distribution monitors: Connecting pollutant properties to visibility via Mie scattering calculations

Chapter 26 - Applying and Interpreting Air Quality Monitoring Data

According to emissions data reported to the United States Environmental Protection Agency (USEPA) and the Texas Commission on Environmental Quality (TCEQ), the BP Products North America Inc. (BP) Texas City Refinery is the worst polluter of all the industries in Texas City and is one of the worst polluting refineries in the country. The facility has reported releasing substantial emissions of Sulfur Dioxide (SO2) and Volatile Organic Compounds (VOCs). Air dispersion modeling of the emissions from the facility can be used to assess contamination in the community surrounding the BP Texas City Refinery. In the present study, air dispersion modeling of SO2 and VOCs was performed using AERMOD, the EPA-preferred regulatory dispersion model, to determine the impact of BP’s contribution to local air pollution on residents of Texas City and La Marque. SO2 emissions reported to be released by the facility were modeled using AERMOD and it was determined that geographical locations inside this plume experienced ambient air concentrations of SO2 meeting or exceeding 50 micrograms per cubic meter (µg/m3) in 2009 and 2010. Data collected by six active SO2 air monitors located in Texas City support the air dispersion modeling results. Additional modeling was conducted for VOCs emitted by the facility in 2010. The AERMOD analysis of SO2 concentrations in and around the BP facility produced results consistent with data collected by the air monitoring network in Texas City. This confirms the accuracy of AERMOD’s estimations and its reliability as an emissions modeling tool. VOC concentrations available for analysis from the air monitoring network in Texas City are extremely limited in terms of the quantity of VOCs sampled for. This evidence affirms the ability of AERMOD to demonstrate comprehensive contaminant impacts that surpass the ability of the current air monitoring network.

Optimization of air monitoring networks using chemical transport model and search algorithm