Soil contamination in nearby natural areas mirrors that in urban greenspaces worldwide

Abstract

Soil contamination is one of the main threats to ecosystem health and sustainability. Yet little is known about the extent to which soil contaminants differ between urban greenspaces and natural ecosystems. Here we show that urban greenspaces and adjacent natural areas (i.e., natural/semi-natural ecosystems) shared similar levels of multiple soil contaminants (metal(loid)s, pesticides, microplastics, and antibiotic resistance genes) across the globe. We reveal that human influence explained many forms of soil contamination worldwide. Socio-economic factors were integral to explaining the occurrence of soil contaminants worldwide. We further show that increased levels of multiple soil contaminants were linked with changes in microbial traits including genes associated with environmental stress resistance, nutrient cycling, and pathogenesis. Taken together, our work demonstrates that human-driven soil contamination in nearby natural areas mirrors that in urban greenspaces globally, and highlights that soil contaminants have the potential to cause dire consequences for ecosystem sustainability and human wellbeing.

Fig. 1. Changes (%) in soil contaminants in urban greenspaces compared with adjacent natural areas worldwide.

A response ratios of (site-level described in Methods) of standardized contaminant indices of metal(loid)s, pesticides, microplastics (MPs), and antibiotic resistance genes (ARGs). B the changes (site-level response ratios, mean ± 95% confidence interval) in the selected soil contaminants within the four categories (i.e., metal(loid)s, pesticides, MPs, and ARGs) and multi-contamination (represented by different colors), comparing urban greenspaces to adjacent natural areas (see Supplementary Fig. 3 for the concentrations of soil contaminants). Multi-contamination represents an average contamination index of standardized four categories of soil contaminants. Complementary figures showing results using an independent approach can be seen in Supplementary Fig. 3, 7 and 10, 11. The maps in the figure were generated using ArcGIS 10.2 software.

Fig. 2. The relative importance of socio-economic and environmental factors on the accumulation of soil contaminants.

A structural equation models reveal direct and indirect associations of examined socio-economic (population size and density, human development index (HDI), gross domestic product (GDP)) and environmental factors (climate, plant cover, and soil) with indices of metal(loid)s (n = 112 plots), pesticides (n = 54 plots), microplastics (MPs) (n = 64 plots) and antibiotic resistance genes (ARGs) (n = 112 plots) in the soil accounting for different management practices (irrigated, mowed and fertilized). Numbers adjacent to arrows are indicative of the effect size of the relationship. B the relative importance of examined human and natural factors on the four groups of soil contaminants, based on linear mixed effects model. Orange and green boxes indicate human and natural factors, respectively. MAT mean annual temperature, MAP mean annual precipitation, SOC soil organic carbon, TP total phosphorus, TN total nitrogen. Urban urban greenspaces, Plant plant cover.

Fig. 3. Associations between soil contaminants and functional soil microbiome.

A proportions of the genes associated with metal(loid) resistance. Asterisks indicate significant differences between urban and natural ecosystems according to nested PERMANOVA using a block design as described in the Method section. *P < 0.05; **P < 0.01. Detailed statistical results for the PERMANOVA are shown in Supplementary Table 8. B the fitted linear relationships between multi-contamination (standardized between 0 and 1, see Methods) and proportions of the selected functional genes, which are related to stress resistance (black), nutrient cycling (red), pathogenesis (green), and microbial metabolism (blue). Statistical analysis was performed using ordinary least squares linear regressions; P < 0.05; n = 32. C a correlation network displaying relationships between soil contaminants and selected functional genes associated with well-known stress resistance, pathogenicity, nutrient cycling and microbial metabolisms. These functional genes were annotated according to BacMet and MG-RAST using metagenomic data (see Methods and Supplementary Table 9). MPs microplastics, ARGs antibiotic resistance genes.