Low-cost biomonitoring and high-resolution, scalable models of urban metal pollution.

As the global toll on human lives and ecosystems exacted by urban pollution grows, planning tools still lack the resolution to identify priority sites where toxic pollution can be most efficiently averted at a spatial scale that matches funding and management. Here we tackle this gap by demonstrating novel scalable methods to monitor and predict urban metal pollution at high resolution (<5 m) across large areas (10,000-100,000 km) to guide pollution reduction and stormwater management. We showcase and calibrate predictive models of Zn, Cu, and a synthetic index of pollution for the Puget Sound region of Washington State, U.S., a densely urbanized yet important ecosystem of conservation interest, and exemplify their transferability across the entire United States. We leveraged widely and freely available datasets of car traffic characteristics and land use as predictor variables and trained the models with biological monitoring data of metal concentrations in epiphytic moss from >100 trees based on new rapid and low-cost protocols introduced in this study. Our model predictions, showing that 50% of the total Cu and Zn pollution across the Puget Sound watershed is deposited over only 3.3% of the land area, will allow cities to effectively and efficiently target toxic hotspots.