Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.

“Can we walk?” Environmental supports for physical activity in India

India is currently facing a non-communicable disease epidemic. Physical activity (PA) is a preventative factor for non-communicable diseases. Understanding the role of the built environment (BE) to facilitate or constrain PA is essential for public health interventions to increase population PA. The objective of this study was to understand BEs associations with PA occurring in two major life domains or life areas—travel and leisure—in urban India. Between December 2014 and April 2015, in-person surveys were conducted with participants (N = 370; female = 47.2%) in Chennai, India. Perceived BE characteristics regarding residential density, land use mix-diversity, land use mix-access, street connectivity, infrastructure for walking and bicycling, aesthetics, traffic safety, and safety from crime were measured using the adapted Neighborhood Environment Walkability Scale-India (NEWS-India). Self-reported PA was measured the International Physical Activity Questionnaire. High residential density was associated with greater odds of travel PA (aOR = 1.9, 95% CI = 1.2, 3.2). Land use mix-diversity was positively related to travel PA (aOR = 2.1, 95%CI = 1.2, 3.6), but not associated with leisure or total PA. The aggregate NEWS-India score predicted a two-fold increase in odds of travel PA (aOR = 1.9, 95% CI = 1.1, 3.1) and a 40% decrease in odds of leisure PA (aOR = 0.6, 95% CI = 0.4, 1.0). However, the association of the aggregated score with leisure PA was not significant. Results suggest that relationships between BE and PA in low-and-middle income countries may be context-specific, and may differ markedly from higher income countries. Findings have public health implications for India suggesting that caution should be taken when translating evidence across countries.