Air Quality Modelling for Ireland

Air pollution is the primary environmental cause of premature death in the EU (European Commission, 2013) and the most problematic pollutants across Europe have consistently been oxides of nitrogen (e.g. nitrogen dioxide (NO2)), particulate matter (e.g. PM10, PM2.5) and ozone (O3). While measurements form an important aspect of air quality assessment, on their own they are unlikely to be sufficient to provide an accurate spatial and temporal description of the pollutant concentrations for exposure assessment and moreover they cannot provide information regarding future air quality. Annex XVI of 2008/50/EC requires member states to “ensure that up to date information on ambient concentrations of the pollutants covered” by the Directive are “made available to the public”. This information must include actual or predicted exceedances of alert and information thresholds and a forecast for the following day of which a model is an integral part. As a result, air quality models are increasingly required for public information, air quality management and research purposes. The primary objectives of this research fellowship were to develop a calibrated air quality forecast model for Ireland capable of predicting the Air Quality Index for Health (AQIH) in each of the air quality zones in Ireland and to model the spatial variation in concentrations on a national scale. This research project has produced three different models for NO2, PM10, PM2.5, O3 and SO2, all of which are available for further use. These are: A hybrid point wise 48 hour forecast model; Spatial model (WS-LUR) to produce annual mean maps of air pollution on a national scale; Temporal WS-LUR model. A comprehensive review of modelling systems carried out at the outset of this research fellowship, together with consideration for key EPA objectives, informed the direction of model development. This review is available as a separate EPA report. A priority within the EPA was to produce air quality forecasts based on the AQIH. The AQIH is based on point wise measurements and in order to extrapolate these measurements to the future, statistical modelling was deemed the most suitable. The advantages of this approach were that it could be developed from first principles specific to the area of interest and completely (avoiding any reliance on a third party to supply the model or apply licensing restrictions) and the associated speed of forecast computation. Forecasts are only useful if they can be computed and made available to the public relatively quickly. The accuracy of such methods also tends to be high and of low bias as they are developed site-specifically unlike large scale deterministic models that are often developed and tested in vastly differing domains. In particular, this method was capable of producing accurate point wise forecasts without the need for a detailed emissions inventory. At the project outset, the emissions inventory was not of sufficient spatial resolution to make realistic point wise forecasts in all air quality zones by deterministic means and it would have been an inefficient use of resources to base the development of forecasts on what was currently available. Initial model development proceeded using time series analysis in conjunction with non-parametric kernel regression, with local meteorological parameters as predictor variables. A model validation study found that this technique produced accurate forecast of ozone and SO2 but had a tendency to under predict peak NO2 and PM10/2.5 concentrations. An analysis of air mass history using the HYSPLIT model was carried out which revealed certain air masses (primarily easterly and re-circulated air) were responsible for most incidence of elevated concentrations. The results of this study were used to develop a HYSPLIT add-on for the forecast model which operates by forecasting air mass history in real time and invoking a different forecasting methodology depending on the region of origin of the air. The ability of the hybrid point wise model to predict daily maximum hourly NO2, SO2, 8 hourly ozone and daily average PM10 and PM2.5 was demonstrated by comparing a full year of modelled data with measured data at each of the AQIH sites. Index of agreement values ranged from a low of 0.80 for SO2 to 0.88 for NO2 and ozone, while correlation coefficients ranged from a low of 0.69 for SO2 to 0.82 for NO2. Full results of this validation study are contained in a separate report. In order to provide detail on the spatial variation of concentration levels across the country, land use regression (LUR) was recommended in the model review as the most suitable technique. This technique uses surrogate spatial indicators to explain the variation in concentration levels between monitoring points. Land cover data (CORINE), DTM output, road density information and population data are all factors that influence concentration levels and data that were broadly available. In contrast to most LUR studies, circular buffers were not used in the determination of spatial predictor variables. Rather, a novel sector based approach (WS-LUR) was adopted whereby variables were calculated within 8 pre-defined sectors representing the major wind directions around each monitoring site. This approach had a dual purpose. Firstly, it accounts for the direction of influence of emission sources on air quality in a given location. Traditional LUR assumes equal influence of emissions in the area surrounding a monitoring site regardless of wind direction. This approximation may be reasonable in highly urbanised areas where emissions sources are relatively uniform in the surrounding region. However, in this study the regression was applied on a national scale and prevailing winds coupled with clear directional influenced at air quality monitoring sites meant that WS-LUR is a superior option. The second advantage of this methodology is that it increases the effective number of data points available for the regression analysis, resulting in a more robust final equation. In conjunction with research project (2013-EH-FS-7), a set of annual mean maps within a geographic information system (GIS) environment were created and validated for each of NO2, PM10, PM2.5, O3 and SO2. These provide a highly relevant source of information regarding spatial variation in concentration levels on a national scale which can be used not only for exposure studies and general air quality assessment, but also as a tool to correlate emission sources and surrogates with air quality. A temporal WS-LUR model was developed for NO2, Ozone and PM10 by including hourly meteorological data in conjunction with pre-specified spatial data as predictor variables. This model has the potential to provide fast, efficient national air quality forecast maps for Ireland with minimal computational requirements. This project has achieved key EPA objectives and has produced a fully automated and operational air quality model which produced twice-daily forecasts of the AQIH in each air quality zone in Ireland. The stepwise approach chosen for model development allowed deliverables prior to completion of the project while minimising associated risks. The models developed as part of this fellowship form solid building blocks on which future air quality modelling studies in Ireland can be based

Short Term Forecasting of Nitrogen Dioxide (NO2) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with airmass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO2 events, which tend to be the episodes ofmost significance in Ireland. Airmass history modelling and k-means clustering are used to identify air mass types that lead to high NO2 levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO2 predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO2 and additional pollutants at new locations/countries where historical monitoring data are available

Maximizing the spatial representativeness of NO2 monitoring data using a combination of local wind-based sectoral division and seasonal and diurnal correction factors

This article describes a new methodology for increasing the spatial representativeness of individual monitoring sites. Air pollution levels at a given point are influenced by emission sources in the immediate vicinity. Since emission sources are rarely uniformly distributed around a site, concentration levels will inevitably be most affected by the sources in the prevailing upwind direction. The methodology provides a means of capturing this effect and providing additional information regarding source/pollution relationships. The methodology allows for the division of the air quality data from a given monitoring site into a number of sectors or wedges based on wind direction and estimation of annual mean values for each sector, thus optimising the information that can be obtained from a single monitoring station. The method corrects for short-term data, diurnal and seasonal variations in concentrations (which can produce uneven weighting of data within each sector) and uneven frequency of wind directions. Significant improvements in correlations between the air quality data and the spatial air quality indicators were obtained after application of the correction factors. This suggests the application of these techniques would be of significant benefit in land-use regression modelling studies. Furthermore, the method was found to be very useful for estimating long-term mean values and wind direction sector values using only short-term monitoring data. The methods presented in this article can result in cost savings through minimising the number of monitoring sites required for air quality studies while also capturing a greater degree of variability in spatial characteristics. In this way, more reliable, but also more expensive monitoring techniques can be used in preference to a higher number of low-cost but less reliable techniques. The methods described in this article have applications in local air quality management, source receptor analysis, land-use regression mapping and modelling and population exposure studies

A Land Use Regression Model for Explaining Spatial Variation in Air Pollution Levels using a Wind Sector Based Approach

Estimating pollutant concentrations at a local and regional scale is essential for good ambient air quality information in environmental and health policy decision making. Here we present a land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce viable air quality maps. The methodology partitions concentration time series from a national FSM network into wind-dependent sectors or “wedges”. A LUR model is derived using predictor variables calculated within the directional wind sectors, and compared against the long-term average concentrations within each sector. This study demonstrates the value of incorporating the relative position of emission source and receptor into the empirical LUR model structure. In our specific application, a model based on 15 FSM training sites captured 78% of the spatial variability in NO2 across the Republic of Ireland. This compares favourably to traditional LUR models based on purpose-designed monitoring campaigns despite using approximately half the number of monitoring points in model development. We applied the LUR equation at a high-resolution across the Republic of Ireland to enable applications such as the study of environmental exposure and human health, assessing representativeness of air quality monitoring networks and informing environmental management and policy makers

Hydrology and communities: a hydrogeological study of Irish Holy wells

As part of efforts to improve water management in Ireland there is a need to promote greater awareness of groundwater issues amongst the general population. One means by which hydrogeologists can engage and educate local communities about groundwater issues is by addressing topics of direct interest to them, such as holy wells. In Ireland there are more than 3,000 holy wells, many of which are sites of devotion, especially on the pattern or saint’s day. Some of these holy wells are visited by pilgrims seeking a particular cure for an ailment (eye cures, backache, toothache are common examples). Many have been associated with a more generic power of healing and wellbeing. Whilst Irish holy wells have been the subject of scholarly study in disciplines such as archaeology, history, geography and cultural studies, very little attention has been given to their hydrogeological settings. This paper describes the results of a hydrogeological characterisation of Irish holy wells, based on a GIS analysis supplemented by field surveys of more than 200 wells. It was found that holy wells occur in most types of geology in Ireland, and are common in the less productive as well as in the regionally important aquifers. Importantly, however, they occur more frequently in areas classed as having extreme or high groundwater vulnerability to pollution; this is consistent with the observation that many holy wells are in fact springs or shallow wells constructed around springs or groundwater seepages. Many holy wells have been lost through neglect or through various development projects, and there is a need to preserve important wells as part of our geological as well as our cultural heritage. The current high levels of interest in holy wells amongst certain communities may warrant a move towards some more formal protection of these wells as a community resource

An Assessment of Contamination Fingerprinting Techniques for Determining the Impact of Domestic Wastewater Treatment Systems on Private Well Supplies

Private wells in Ireland and elsewhere have been shown to be prone to microbial contamination with the main suspected sources being practices associated with agriculture and domestic wastewater treatment systems (DWWTS). While the microbial quality of private well water is commonly assessed using faecal indicator bacteria, such as Escherichia coli, such organisms are not usually source-specific, and hence cannot definitively conclude the exact origin of the contamination. This research assessed a range of different chemical contamination fingerprinting techniques (ionic ratios, artificial sweeteners, caffeine, fluorescent whitening compounds, faecal sterol profiles and pharmaceuticals) as to their use to apportion contamination of private wells between human wastewater and animal husbandry wastes in rural areas of Ireland. A one-off sampling and analysis campaign of 212 private wells found that 15% were contaminated with E. coli. More extensive monitoring of 24 selected wells found 58% to be contaminated with E. coli on at least one occasion over a 14-month period. The application of fingerprinting techniques to these monitored wells found that the use of chloride/bromide and potassium/sodium ratios is a useful low-cost fingerprinting technique capable of identifying impacts from human wastewater and organic agricultural contamination, respectively. The artificial sweetener acesulfame was detected on several occasions in a number of monitored wells, indicating its conservative nature and potential use as a fingerprinting technique for human wastewater. However, neither fluorescent whitening compounds nor caffeine were detected in any wells, and faecal sterol profiles proved inconclusive, suggesting limited suitability for the conditions investigated

“Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution

Air pollution is now recognized as the world’s single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed “Active Population Exposure” was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to “Home Population Exposure”, which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the “Active Population Exposure” scenario. Population-weighted exposure computed in each district of NYC for the “Active” scenario were found to be statistically significantly (p < 0.05) different to the “Home” scenario for most districts. In investigating the temporal variability of the “Active” population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health

A global perspective on assessing groundwater quality

An assessment of global groundwater quality is needed in response to the threats posed by anthropogenic and geogenic contaminants. This essay summarises the challenges involved, including a large number of potentially relevant water quality parameters, the poor availability of data in many regions and the complex nature of groundwater systems. Direct monitoring data can sometimes be augmented by indirect methods such as earth observations, and by involving citizen science. A new web portal is being developed to complement existing databases

Groundwater quality: global challenges, emerging threats and novel approaches

Improving our understanding of groundwater quality threats to human health and the environment is essential to protect and manage groundwater resources effectively. This essay highlights some global groundwater quality challenges, describes key contaminant groups and threats of emerging concern, including antimicrobial resistance, and discusses novel approaches to assessing groundwater quality. Groundwater quality monitoring needs to improve significantly in order to effectively identify and mitigate threats to groundwater from historical, current and future pollution

Groundwater quality: global challenges, emerging threats and novel approaches

Improving our understanding of groundwater quality threats to human health and the environment is essential to protect and manage groundwater resources effectively. This essay highlights some global groundwater quality challenges, describes key contaminant groups and threats of emerging concern, including antimicrobial resistance, and discusses novel approaches to assessing groundwater quality. Groundwater quality monitoring needs to improve significantly in order to effectively identify and mitigate threats to groundwater from historical, current and future pollution