Sarah Lindley

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Adaptation Strategies for Climate Change in the Urban Environment: Assessing Climate Change Related Risk in UK Urban Areas

This paper presents a conurbation‐scale risk assessment methodology which aims to provide a screening tool to assist with planning for climate change‐related risks in the urban environment. This work has been undertaken as part of a wider, interdisciplinary project, Adaptation Strategies for Climate Change in the Urban Environment (ASCCUE). The main focus of ASCCUE is to help improve understanding of the consequences of climate change for urban areas and how these, and the neighbourhoods within them, can best be adapted. Adaptation options will be explored in the context of both conurbation‐scale strategic planning and neighbourhood‐level urban design. The paper conveys some of the initial outputs from the ASCCUE project. It firstly presents the overarching risk assessment framework, before outlining the GIS‐based elements of the methodology. This draws on a characterisation of the urban area into distinctive “urban morphology units” as the spatial framework for the analytical work. An example of heat related risk is given by way of an illustrative application of the methodology. The paper concludes with a consideration of the limitations of the approach and how some of these will be tackled as part of the ongoing work programme.

A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project

The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter <10 μm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4–5 and 8–10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8–10 years and exposure at the birth address (n=10377): 1.10 (0.81–1.49) per 10 μg·m-3 nitrogen dioxide; 0.88 (0.63–1.24) per 10 μg·m-3 PM10; 1.23 (0.78–1.95) per 5 μg·m-3 PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts. No significant association between air pollution and childhood asthma prevalence in five European birth cohorts http://ow.ly/Cdbba

Assessing nature’s contributions to people

Recognizing culture, and diverse sources of knowledge, can improve assessments A major challenge today and into the future is to maintain or enhance beneficial contributions of nature to a good quality of life for all people. This is among the key motivations of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), a joint global effort by governments, academia, and civil society to assess and promote knowledge of Earths biodiversity and ecosystems and their contribution to human societies in order to inform policy formulation. One of the more recent key elements of the IPBES conceptual framework (1) is the notion of natures contributions to people (NCP), which builds on the ecosystem service concept popularized by the Millennium Ecosystem Assessment (MA) (2). But as we detail below, NCP as defined and put into practice in IPBES differs from earlier work in several important ways. First, the NCP approach recognizes the central and pervasive role that culture plays in defining all links between people and nature. Second, use of NCP elevates, emphasizes, and operationalizes the role of indigenous and local knowledge in understanding natures contribution to people.

Rainwater runoff retention on an aged intensive green roof

Urban areas are characterised by large proportions of impervious surfaces which increases rainwater runoff and the potential for surface water flooding. Increased precipitation is predicted under current climate change projections, which will put further pressure on urban populations and infrastructure. Roof greening can be used within flood mitigation schemes to restore the urban hydrological balance of cities. Intensive green roofs, with their deeper substrates and higher plant biomass, are able to retain greater quantities of runoff, and there is a need for more studies on this less common type of green roof which also investigate the effect of factors such as age and vegetation composition. Runoff quantities from an aged intensive green roof in Manchester, UK, were analysed for 69 rainfall events, and compared to those on an adjacent paved roof. Average retention was 65.7% on the green roof and 33.6% on the bare roof. A comprehensive soil classification revealed the substrate, a mineral soil, to be in good general condition and also high in organic matter content which can increase the water holding capacity of soils. Large variation in the retention data made the use of predictive regression models unfeasible. This variation arose from complex interactions between Antecedant Dry Weather Period (ADWP), season, monthly weather trends, and rainfall duration, quantity and peak intensity. However, significantly lower retention was seen for high rainfall events, and in autumn, which had above average rainfall. The study period only covers one unusually wet year, so a longer study may uncover relationships to factors which can be applied to intensive roofs elsewhere. Annual rainfall retention for Manchester city centre could be increased by 2.3% by a 10% increase in intensive green roof construction. The results of this study will be of particular interest to practitioners implementing greenspace adaptation in temperate and cool maritime climates.

Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort.

Background: Findings from previous studies on the effects of air pollution exposure on lung function during childhood have been inconsistent. A common limitation has been the quality of exposure data used, and few studies have modeled exposure longitudinally throughout early life. Objectives: We sought to study the long-term effects of exposure to particulate matter with an aerodynamic diameter ≤ 10 μm (PM10) and to nitrogen dioxide (NO2) on specific airway resistance (sRaw) and forced expiratory volume in 1 sec (FEV1) before and after bronchodilator treatment. Subjects were from the Manchester Asthma and Allergy Study (MAAS) birth cohort (n = 1,185). Methods: Spirometry was performed during clinic visits at ages 3, 5, 8, and 11 years. Individual-level PM10 and NO2 exposures were estimated from birth to 11 years of age through a microenvironmental exposure model. Longitudinal and cross-sectional associations were estimated using generalized estimating equations and multivariable linear regression models. Results: Lifetime exposure to PM10 and NO2 was associated with significantly less growth in FEV1 (percent predicted) over time, both before (–1.37%; 95% CI: –2.52, –0.23 for a 1-unit increase in PM10 and –0.83%; 95% CI: –1.39, –0.28 for a 1-unit increase in NO2) and after bronchodilator treatment (–3.59%; 95% CI: –5.36, –1.83 and –1.20%; 95% CI: –1.97, –0.43, respectively). We found no association between lifetime exposure and sRaw over time. Cross-sectional analyses of detailed exposure estimates for the summer and winter before 11 years of age and lung function at 11 years indicated no significant associations. Conclusions: Long-term PM10 and NO2 exposures were associated with small but statistically significant reductions in lung volume growth in children of elementary-school age. Citation: Mölter A, Agius RM, de Vocht F, Lindley S, Gerrard W, Lowe L, Belgrave D, Custovic A, Simpson A. 2013. Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort. Environ Health Perspect 121:1232–1238. http://dx.doi.org/10.1289/ehp.1205961

Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Abstract Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.

Metal and nutrient dynamics on an aged intensive green roof

Runoff and rainfall quality was compared between an aged intensive green roof and an adjacent conventional roof surface. Nutrient concentrations in the runoff were generally below Environmental Quality Standard (EQS) values and the green roof exhibited NO3(-) retention. Cu, Pb and Zn concentrations were in excess of EQS values for the protection of surface water. Green roof runoff was also significantly higher in Fe and Pb than on the bare roof and in rainfall. Input-output fluxes revealed the green roof to be a potential source of Pb. High concentrations of Pb within the green roof soil and bare roof dusts provide a potential source of Pb in runoff. The origin of the Pb is likely from historic urban atmospheric deposition. Aged green roofs may therefore act as a source of legacy metal pollution. This needs to be considered when constructing green roofs with the aim of improving pollution remediation.

Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles

Background: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. Objectives: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development. Methods: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. Results: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%). Conclusions: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted. Citation: Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, de Hoogh K, Declercq C, Dimakopoulou K, Eeftens M, Eriksen KT, Forastiere F, Galassi C, Grivas G, Heinrich J, Hoffmann B, Ineichen A, Korek M, Lanki T, Lindley S, Modig L, Mölter A, Nafstad P, Nieuwenhuijsen MJ, Nystad W, Olsson D, Raaschou-Nielsen O, Ragettli M, Ranzi A, Stempfelet M, Sugiri D, Tsai MY, Udvardy O, Varró MJ, Vienneau D, Weinmayr G, Wolf K, Yli-Tuomi T, Hoek G, Brunekreef B. 2014. Performance of multi-city land use regression models for nitrogen dioxide and fine particles. Environ Health Perspect 122:843–849; http://dx.doi.org/10.1289/ehp.1307271