Effects of Brownfield Remediation on Total Gaseous Mercury Concentrations in an Urban Landscape In order to obtain a better perspective of the impacts of brownfields on the landxe2x80x93atmosphere exchange of mercury in urban areas, total gaseous mercury (TGM) was measured at two heights (1.8 m and 42.7 m) prior to 2011xe2x80x932012 and after 2015xe2x80x932016 for the remediation of a brownfield and installation of a parking lot adjacent to the Syracuse Center of Excellence in Syracuse, NY, USA. Prior to brownfield remediation, the annual average TGM concentrations were 1.6 xc2xb1 0.6 and 1.4 xc2xb1 0.4     ng xc2xb7  m  xe2x88x92 3       at the ground and upper heights, respectively. After brownfield remediation, the annual average TGM concentrations decreased by 32% and 22% at the ground and the upper height, respectively. Mercury soil flux measurements during summer after remediation showed net TGM deposition of 1.7     ng xc2xb7  m  xe2x88x92 2   xc2xb7   day   xe2x88x92 1       suggesting that the site transitioned from a mercury source to a net mercury sink. Measurements from the Atmospheric Mercury Network (AMNet) indicate that there was no regional decrease in TGM concentrations during the study period. This study demonstrates that evasion from mercurycontaminated soil significantly increased local TGM concentrations, which was subsequently mitigated after soil restoration. Considering the large number of brownfields, they may be an important source of mercury emissions source to local urban ecosystems and warrant future study at additional locations.

Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable XRay Fluorescence Measurements Portable Xray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale crossvalidation of the models gave the following R2 values for predictions of Cu (R2  0.63), Zn (R2  0.92), and Cd (R2  0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R2 u003e 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the nonlinear models, especially MARS, were more accurate than MLR for Cu (R2  0.94) and Cd (R2  0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.