A multi-media monitoring field investigation, which included atmospheric, road sediment and soil samples, was carried out at two highway study sites to identify past and present Pb sources. Past Pb anthropogenic sources such as paint and leaded gasoline were linked to significant Pb accumulation in roadside soils at both sites through Pb isotopic analyses. This was achieved by identifying the distinct Pb isotopic composition in older versus newer Pb accumulation at different depths across the soil profile. Older Pb accumulations exhibited lower ²⁰⁶Pb/²⁰⁷Pb isotopic ratios, consistent with Canadian Pb-bearing ores, whereas newer Pb accumulations reflected a mixture of the ²⁰⁶Pb/²⁰⁷Pb ratios of road sediment samples, with the Pb isotopic signature of uncontaminated soil. Isotopic analyses were also helpful in identifying road sediment as an important current source of Pb in roadside soils, by comparing the isotopic signatures derived from road sediment and atmospheric dustfall. The known association of Pb with anthropogenic sources was used to indirectly relate other metals (Cu, Mn, Zn) to the same source by the Enrichment Ratio method. Significant positive correlations at the 90-95% confidence level were found between Cu, Zn and Pb Enrichment Ratios in roadside and dust deposition samples. Weaker correlations were found between Mn and Pb, at the highway study site with the least amount of traffic. However, correlations between these two metals were significant at the 90% confidence level for the busier highway site highlighting Mn potential anthropogenic source. An isotopic tracer study is suggested to further investigate the process of Mn redistribution in the environment due to exhaust fuel emissions. More research is needed regarding the potential impact from using a Mn-based fuel additive.

Automated synoptic weather typing and robust orthogonal stepwise regression analysis (via principal components analysis) were applied together to develop within-weather-type air pollution prediction models for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO₂, ozone – O₃, sulphur dioxide – SO₂, and suspended particles – SP) for the period 1974–2000 in south-central Canada. The SAS robust regression procedure was used to limit the influence of outliers on air pollution prediction algorithms. Six-hourly Environment Canada surface observed meteorological data and 6-hourly US National Centers for Environmental Prediction (NCEP) reanalysis data of various weather elements were used in the analysis. The models were developed using two-thirds of the total years for meteorological and air pollution data; the remaining one-third (randomly selected) was used for model validation. Robust stepwise regression analysis was performed to analytically determine the meteorological variables that might be used to predict air pollution concentrations. There was a significant correlation between observed daily mean air pollution concentrations and model predictions. About 20, 50, and 80% of the 80 prediction models across the study area possessed R ² values ≥ 0.7, 0.6, and 0.5, respectively. The results of model validation were similar to those of model development, with slightly smaller model R ² values.

Using within-weather-group air pollution prediction models developed in Part I of this research, this study estimates future air pollution levels for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO₂, ozone – O₃, sulphur dioxide – SO₂, and suspended particles – SP) under future climate scenarios for four cities in south-central Canada. A statistical downscaling method was used to downscale five general circulation model (GCM) scenarios to selected weather stations. Downscaled GCM scenarios were used to compare respective characteristics of the weather groups developed in Part I; discriminant function analysis was used to allocate future days from two windows of time (2040–2059 and 2070–2089) into one of four weather groups. In Part I, the four weather groups were characterised as hot, cold, air pollution-related, and other (defined as relatively good air quality and comfortable weather conditions). In estimating future daily air pollution concentrations, three future pollutant emission scenarios were considered: Scenario I – emissions decreasing 20% by 2050, Scenario II – future emissions remaining at the same level as at the end of the twentieth century, and Scenario III – emissions increasing 20% by 2050. The results showed that, due to increased temperatures, the average annual number of days with high O₃ levels in the four selected cities could increase by more than 40–100% by the 2050s and 70–200% by the 2080s (from the current areal average of 8 days) under the three pollutant emission scenarios. The corresponding number of low O₃ days could decrease by 4–10% and 5–15% (from the current areal average of 312 days). For the rest of the pollutants, future air pollution levels will depend on future pollutant emission levels. Under emission Scenarios II and III, the average annual number of high pollution days could increase 20–40% and 80–180%, respectively, by the middle and late part of this century. In contrast, under Scenario I, the average annual number of high pollution days could decrease by 10–65%.

Groundwater contamination by leakage and spill of petroleum hydrocarbons from underground storage tanks has been a major environmental concern. Among various remediation alternatives, the vacuum-enhanced free product recovery (VFPR) is an important technology to extract light nonaqueous-phase liquids (LNAPLs) from subsurface. However, efficient design of a VFPR system was challenging to practitioners, since the process of hydrocarbon removal is costly and time consuming. To address such a problem, an integrated study system for optimizing the VFPR process was developed through coupling a numerical modeling system, a multivariate regression technique and nonlinear optimization model into a general framework. A two-dimensional multiphase flow simulation system was provided for modeling VFPR processes. An iterative stepwise-inference regression (ISIR) method was advanced for establishing a linkage between remediation actions and system responses. A nonlinear optimization model embedded with ISIR was then established for generating desired operating conditions. The results from a case study demonstrated that the established optimization model could effectively analyze tradeoffs between various environmental and economical considerations, and provide effective decision supports for site remediation practices. Compared with the conventional stepwise-cluster analysis method, the proposed ISIR method was more efficient and reliable in approximating relationships between remediation actions and system responses, and could significantly enhance the robustness of optimization solutions.