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    Relocating croplands could drastically reduce the environmental impacts of global food production

    We use the notation in Table 1.Table 1 Notation used in the description of the optimisation framework.Full size tableCurrent crop production and areas, P
    i(x), H
    i(x)We used 5-arc-minute maps of the fresh-weight production Pi(x) (Mg year−1) and cropping area Hi(x) (ha) of 25 major crops (Table 2) in the year 201037. These represent the most recent spatially explicit and crop-specific global data75. Separate maps were available for irrigated and rainfed croplands, allowing us to estimate the worldwide proportion of irrigated areas as 21% of all croplands.Table 2 Crops included in the analysis.Full size tableAgro-ecologically attainable yields ({widehat{Y}}_{i}(x))
    We used 5-arc-minute maps of the agro-ecologically attainable dry-weight yield (Mg ha −1 year−1) of the same 25 crops on worldwide potential growing areas (Supplementary Movie 3) from the GAEZ v4 model, which incorporates thermal, moisture, agro-climatic, soil, and terrain conditions42. These yield estimates were derived based on the assumption of rainfed water supply (i.e., without additional irrigation) and are available for current climatic conditions and, assuming a CO2 fertilisation effect, for four future (2071–2100 period) climate scenarios corresponding to representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.576 simulated by the HadGEM2-ES model77. Potential rainfed yield estimates for current climatic conditions were available for a low- and a high-input crop management level, representing, respectively, subsistence-based organic farming systems and advanced, fully mechanised production using high-yielding crop varieties and optimum fertiliser and pesticide application42. We additionally considered potential yields representing a medium-input management scenario, given by the mean of the relevant low- and high-input yields. Future potential yields were available only for the high-input management level. Thus, we considered a total of 175 (=25 × 3 present + 25 × 4 future) potential yield maps. Potential dry-weight yields were converted to fresh-weight yields, ({widehat{Y}}_{i}(x)), using crop-specific conversion factors42,78.Both current and future potential rainfed yields from GAEZ v4 were simulated based on daily weather data, and therefore account for short-term events such as frost days, heat waves, and wet and dry spells42. However, the estimates represent averages of annual yields across 30-year periods; thus, whilst the need for irrigation on cropping areas identified in our approach during particularly dry years may in principle be obviated by suitable storage of crop production79, in practice, ad hoc irrigation may be an economically desirable measure to maintain productivity during times of drought, which are projected to increase in different geographic regions due to climate change80,81.Carbon impact C
    i(x)Following an earlier approach8, the carbon impact of crop production, Ci(x), in a 5-arc-minute grid cell was estimated as the difference between the potential natural carbon stocks and the cropland-specific carbon stocks, each given by the sum of the relevant vegetation- and soil-specific carbon. The change in vegetation carbon stocks resulting from land conversion is given by the difference between carbon stored in the potential natural vegetation, available as a 5-arc-minute global map8 (Supplementary Fig. 1a), and carbon stored in the crops, for which we used available estimates8,78. Regarding soil, spatially explicit global estimates of soil organic carbon (SOC) changes from land cover change are not available. We therefore chose a simple approach, consistent with estimates across large spatial scales, rather than a complex spatially explicit model for which, given the limited empirical data, robust predictions across and beyond currently cultivated areas would be difficult to achieve. Following an earlier approach8, and supported by empirical meta-analyses82,83,84,85,86, we assumed that the conversion of natural habitat to cropland results in a 25% reduction of the potential natural SOC. For the latter, we used a 5-arc-minute global map of pre-agricultural SOC stocks7 (Supplementary Fig. 1b). Thus, the total local carbon impact (Mg C ha−1) of the production of crop i in the grid cell x was estimated as$${C}_{i}(x)={{C}}_{{{{{{rm{potential}}}}}},{{{{{rm{vegetation}}}}}}}(x)+0.25cdot {C}_{{{{{{rm{potential}}}}}},{{{{{rm{SOC}}}}}}}(x)-{C}_{{{{{{rm{crop}}}}}}}(i)$$
    (1)
    where ({{C}}_{{{{{{rm{potential}}}}}},{{{{{rm{vegetation}}}}}}}(x)) and ({C}_{{{{{{rm{potential}}}}}},{{{{{rm{SOC}}}}}}}(x)) denote the potential natural carbon stocks in the vegetation and the soil in x, respectively, and ({C}_{{{{{{rm{crop}}}}}}}(i)) denotes the carbon stocks of crop i (all in Mg C ha−1). By design, the approach allows us to estimate the carbon impact of the conversion of natural habitat to cropland regardless of whether an area is currently cultivated or not.In our analysis, we did not consider greenhouse gas emissions from sources other than from land use change, including nitrous emissions from fertilised soils and methane emissions from rice paddies87. In contrast to the one-off land use change emissions considered here, those are ongoing emissions that incur continually in the production process. We would assume that the magnitude of these emissions in a scenario of redistribution of agricultural areas, in which the total production of each crop remains constant, is roughly similar to that associated with the current distribution of areas. We also did not consider emissions associated with transport; however, these have been shown to be small compared to other food chain emissions88 and poorly correlated with the distance travelled by agricultural products89.Biodiversity impact B
    i(x)Analogous to our approach for carbon, we estimated the biodiversity impact of crop production, Bi(x), in a 5-arc-minute grid cell as the difference between the local biodiversity associated with the natural habitat and that associated with cropland. For our main analysis, we quantified local biodiversity in terms of range rarity (given by the sum of inverse species range sizes; see below) of mammals, birds, and amphibians. Range rarity has been advocated as a biodiversity measure particularly relevant to conservation planning in general39,90,91,92,93 and the protection of endemic species in particular39. In a supplementary analysis, we additionally considered biodiversity in terms of species richness.We used 5-arc-minute global maps of the range rarity and species richness of mammals, birds, and amphibians under potential natural vegetation (Supplementary Fig. 1c, d) and under cropland land cover94. The methodology used to generate these data38 combines species-specific extents of occurrence (spatial envelopes of species’ outermost geographic limits40) and habitat preferences (lists of land cover categories in which species can live95), both available for all mammals, birds, and amphibians96,97, with a global map of potential natural biomes44 in order to estimate which species would be present in a grid cell for natural habitat conditions. Incorporating information on species’ ability to live in croplands, included in the habitat preferences, allows for determining the species that would, and those that would not, tolerate a local conversion of natural habitat to cropland. The species richness impact of crop production in a grid cell is then obtained as the number of species estimated to be locally lost when natural habitat is converted to cropland. Instead of weighing all species equally, the range rarity impact in a grid cell is calculated as the sum of the inverse potential natural range sizes of the species locally lost when natural habitat is converted; thus, increased weight is attributed to range-restricted species, which tend to be at higher extinction risk40,41.As in the case of carbon, the approach allows us to estimate the biodiversity impact of crop production in both currently cultivated and uncultivated areas.Land potentially available for agriculture, V(x)We defined the area V(x) (ha) potentially available for crop production in a given grid cell x, as the area not currently covered by water bodies42, land unsuitable due to soil and terrain constraints42, built-up land (urban areas, infrastructure, roads)1, pasture lands1, crops not considered in our analysis37, or protected areas42 (Supplementary Fig. 1e). In the scenario of a partial relocation of crop production, in which a proportion of existing croplands is not moved, the relevant retained areas are additionally subtracted from the potentially available area, as described further below.Optimal transnational relocationWe first consider the scenario in which all current croplands are relocated across national borders based on current climate (Fig. 3a, dark blue line). For each crop i and each grid cell x, we determined the local (i.e., grid-cell-specific) area ({widehat{H}}_{i}(x)) (ha) on which crop i is grown in cell x so that the total production of each crop i equals the current production and the environmental impact is minimal. Denoting by$${bar{P}}_{i}={sum }_{x}{P}_{i}(x)$$
    (2)
    the current global production of crop i, any solution ({widehat{H}}_{i}(x)) must satisfy the equality constraints$${sum }_{x}{widehat{H}}_{i}(x)cdot {widehat{Y}}_{i}(x)={bar{P}}_{{{{{{rm{i}}}}}}},{{{{{rm{for}}}}}}quad{{{{{rm{each}}}}}},{{{{{rm{crop}}}}}},i$$
    (3)
    requiring the total production of each individual crop after relocation to be equal to the current one. A solution must also satisfy the inequality constraints$${sum }_{i}{widehat{H}}_{i}(x)le V(x),{{{{{rm{for}}}}}}quad{{{{{rm{each}}}}}},{{{{{rm{grid}}}}}},{{{{{rm{cell}}}}}},x,,$$
    (4)
    ensuring that the local sum of cropping areas is not larger than the locally available area V(x) (see above). Given these constraints, we can identify the global configuration of croplands that minimises the associated total carbon or biodiversity impact by minimising the objective function$${sum }_{x}{widehat{H}}_{i}(x)cdot {C}_{i}(x)to ,{{min }}quad{{{{{rm{or}}}}}}quad{sum }_{x}{widehat{H}}_{i}(x)cdot {B}_{i}(x)to ,{{min }}$$
    (5)
    respectively. More generally, we can minimise a combined carbon and biodiversity impact measure, and examine potential trade-offs between minimising each of the two impacts, by considering the weighted objective function$${sum }_{x}{widehat{H}}_{i}(x)cdot (alpha cdot {C}_{i}(x)+(1-alpha )cdot {B}_{i}(x))to ,{{min }}$$
    (6)
    where the weighting parameter α ranges between 0 and 1.Considering all crops across all grid cells, we denote by$$bar{C}={sum }_{i}{sum }_{x}{H}_{i}(x)cdot {C}_{i}(x)$$
    (7)
    the global carbon impact associated with the current distribution of croplands, and by$$hat{C}(alpha )={sum }_{i}{sum }_{x}{hat{H}}_{i}(x)cdot {C}_{i}(x)$$
    (8)
    the global carbon impact associated with the optimal distribution ({{{widehat{H}}_{i}(x)}}_{i,x}(={{{widehat{H}}_{i}^{alpha }(x)}}_{i,x})) of croplands for some carbon-biodiversity weighting (alpha in [0,1]). The relative change between the current and the optimal carbon impact is then given by$$hat{c}(alpha )=100 % cdot frac{hat{C}(alpha )-bar{C}}{bar{C}}$$
    (9)
    Using analogous notation, the relative change between the current and the optimal global biodiversity impact across all crops and grid cells is given by$$widehat{b}(alpha )=100 % cdot frac{widehat{B}(alpha )-bar{B}}{bar{B}}$$
    (10)
    The dark blue line in Fig. 3a visualises (widehat{c}(alpha )) and (widehat{b}(alpha )) for the full range of carbon-biodiversity weightings (alpha in [0,1]), each of which corresponds to a specific optimal distribution ({{{widehat{H}}_{i}(x)}}_{i,x}) of croplands. We defined an optimal weighting ({alpha }_{{{{{{rm{opt}}}}}}}), meant to represent a scenario in which the trade-off between minimising the total carbon impact and minimising the total biodiversity impact is as small as possible. Such a weighting is necessarily subjective; here, we defined it as$${alpha }_{{{{{{rm{opt}}}}}}}={{arg }},{{{min }}}_{alpha in [0,1]}left|begin{array}{ll}frac{frac{partial {hat{c}}(alpha)} {partial {hat{b}}(alpha)}}{hat{c}(alpha)} cdot frac{frac{partial {hat{b}}(alpha)} {partial {hat{c}}(alpha)}}{hat{b}(alpha)}end{array}right|$$
    (11)
    Each of the two factors on the right-hand side represents the relative rate of change in the reduction of one impact type with respect to the change in the reduction of the other one as α varies. Thus, αopt represents the weighting at which neither impact type can be further reduced by varying α without increasing the relative impact of the other by at least the same amount. Scenarios based on this optimal weighting are shown in Figs. 1,  2, and Supplementary Figs. 3–6, and are represented by the black markers in Fig. 3.Our approach does not account for multiple cropping; i.e., part of a grid cell is not allocated to more than one crop, and the assumed annual yield is based on a single harvest. Allowing for multiple crops to be successively planted in the same location during a growing period would increase the dimensionality of the optimisation problem substantially. However, given that only 5% of current global rainfed areas are under multiple cropping98, this is likely not a strong limitation of our rainfed-based analysis. As a result of this approach, our results may even slightly underestimate local crop production potential and therefore global impact reduction potentials.Optimal national relocationIn the case of areas being relocated within national borders, the mathematical framework is identical with the exception that the sum over relevant grid cells x in Eqs. (2) and (4) is taken over the cells that define the given country of interest, instead of the whole world. In this way, the total production of each crop within each country for optimally distributed croplands is the same as for current areas. The optimisation problem is then solved independently for each country.Optimal partial relocationWhen (either for national or transnational relocation) only a certain proportion (lambda in [0,1]) of the production of each crop (of a country or the world) is being relocated rather than the total production, Eq. (3) changes to$$mathop{sum}limits_{x}{widehat{H}}_{i}(x)cdot {widehat{Y}}_{i}(x)=lambda cdot {bar{P}}_{i},{{{{{rm{for}}}}}},{{{{{rm{each}}}}}},{{{{{rm{crop}}}}}},i,.$$
    (12)
    In addition, the area potentially available for new croplands, V(x), (see above) is reduced by the area that remains occupied by current croplands accounting for the proportion ((1-lambda )) of production that is not being relocated. We denote by ({H}_{i}^{lambda }(x)) the area that continues to be used for the production of crop i in grid cell x in the scenario where the proportion λ of the production is being optimally redistributed. In particular, ({H}_{i}^{0}(x)={H}_{i}(x)) and ({H}_{i}^{1}(x)=0) for all i and x. For a given carbon-biodiversity weighting (alpha in [0,1]) in Eq. (6), ({H}_{i}^{lambda }(x)) is calculated as follows. First, all grid cells in which crop i is currently grown are ordered according to their agro-environmental efficiency, i.e., the grid-cell-specific ratio between the environmental impact attributed to the production of the crop and the local production,$${E}_{i}^{alpha }(x)=frac{{H}_{i}(x)cdot (alpha cdot {C}_{i}(x)+(1-alpha )cdot {B}_{i}(x))}{{P}_{i}(x)}.$$
    (13)
    Let ({x}_{1}(={x}_{1}(i,alpha ))) denote the index of the grid cell in which crop i is currently grow for which ({E}_{i}^{alpha }) is smallest among all grid cells in which the crop is grown. Then let x2 be the index for which ({E}_{i}^{alpha }) is second smallest (or equal to the smallest), and so on. Thus, the vector (({x}_{1},{x}_{2},{x}_{3},ldots )) contains all indices of grid cells where crop i is currently grown in descending order of agro-environmental efficiency. The area ({H}_{i}^{lambda }({x}_{n})) retained in some grid cell ({x}_{n}) is then given by$${H}_{i}^{lambda }({x}_{n})=left{begin{array}{ll}{H}_{i}({x}_{n}) & {{{{{rm{if}}}}}};mathop{sum }limits_{m=1}^{n}{P}_{i}({x}_{m})le (1-lambda )cdot {bar{P}}_{i}\ 0, & hskip-7.5pc{{{{{rm{else}}}}}}end{array}right.$$
    (14)
    Thus, cropping areas in a grid cell ({x}_{n}) are retained if they are amongst the most agro-environmentally efficient ones of crop i on which the combined production does not exceed ((1-lambda )cdot {bar{P}}_{i}) (which is not being relocated). Growing areas in the remaining, less agro-environmental efficient grid cells are abandoned and become potentially available for other relocated crops. Note that ({H}_{i}^{lambda }) depends on the weighting α of carbon against biodiversity impacts. Finally, instead of Eq. (4), we have, in the case of the partial relocation of the proportion λ of the total production,$$mathop{sum}limits_{i}{widehat{H}}_{i}(x)le V(x)-{H}_{i}^{lambda }(x)quad{{{{{rm{for}}}}}},{{{{{rm{each}}}}}},{{{{{rm{grid}}}}}},{{{{{rm{cell}}}}}},x,.$$
    (15)
    Solving the optimisation problemAll datasets needed in the optimisation (i.e., (A(x)), ({P}_{i}(x)), ({H}_{i}(x)), ({C}_{i}(x)), ({B}_{i}(x)), ({widehat{Y}}_{i}(x)), (V(x))) are available at a 5 arc-minute (0.083°) resolution; however, computational constraints required us to upscale these to a 20-arc-minute grid (0.33°) spatial grid. At this resolution, Eq. (6) defines a 1.12 × 106-dimensional linear optimisation problem in the scenario of across-border relocation. The high dimensionality of the problem is in part due to the requirement in Eq. (3) that the individual production level of each crop is maintained. Requiring instead that, for example, only the total caloric production is maintained31,99 reduces Eq. (6) to a 1-dimensional problem. However, in such a scenario, the production of individual crops, and therefore of macro- and micronutrients, would generally be very different from current levels, implicitly assuming potentially drastic dietary shifts that may not be nutritionally or culturally realistic.The optimisation problem in Eq. (6) was solved using the dual-simplex algorithm in the function linprog of the Matlab R2021b Optimization Toolbox100 for a termination tolerance on the dual feasibility of 10−7 and a feasibility tolerance for constraints of 10−4.In the case of a transnational relocation of crop production, the algorithm always converged to the optimal solution, i.e., for all crop management levels, climate scenarios, and proportions of production that were being relocated. For the relocation within national borders, this was not always the case. This is because some countries produce small quantities of crops which, according to the GAEZ v4 potential yield estimates, could not be grown in the relevant quantities anywhere in the country under natural climatic conditions and for rainfed water supply; these crops likely require greenhouse cultivation or irrigation can therefore not be successfully relocated within our framework. Across all countries, this was the case for production occurring on 0.6% of all croplands. When this was the case for a certain country and crop, we excluded the crop from the optimisation routine, and a country’s total carbon and biodiversity impacts were calculated as the sum of the impacts of optimally relocated crops plus the current impacts of non-relocatable crops.This issue is linked to why determining the optimal distribution of croplands within national borders is not a well-defined problem for future climatic conditions. Under current climatic conditions, if a crop cannot be relocated within our framework, then its current distribution offers a fall-back solution that provides the current production level and allows us to quantify environmental impacts. Different climatic conditions in the future mean that the production of a crop across current growing locations will not be the same as it is today, and therefore the fall-back solution available for the present is no longer available, so that a consistent quantification of the environmental impacts of a non-relocatable crop is not possible.Carbon and biodiversity recovery trajectoriesOur analysis in Supplementary Fig. 6 requires spatially explicit estimates of the carbon recovery trajectory on abandoned croplands. Whilst carbon and biodiversity regeneration have been shown to follow certain general patterns, recovery is context-specific (Supplementary Note 1) in that, depending on local conditions, the regeneration in a specific location can take place at slower or faster speeds than would typically be the case in the broader ecoregion. Here, we assumed that these caveats can be accommodated by using conservative estimates of recovery times and by assuming that local factors will average out at the spatial resolution of our analysis. The carbon recovery times assumed here are based on ecosystem-specific estimates of the time required for abandoned agricultural areas to retain pre-disturbance carbon stocks82. Aiming for a conservative approach, we assumed carbon recovery times equal to at least three times these estimates, rounded up to the nearest quarter century (Table 3). Independent empirical estimates from specific sites and from meta-analyses are well within these time scales (Supplementary Note 1).Table 3 Assumed times required for carbon stocks on abandoned cropland to reach pre-disturbance levels.Full size tableApplying the values in Table 3 to a global map of potential natural biomes44 provides a map of carbon recovery times. We assumed a square root-shaped carbon recovery trajectory across these regeneration periods101; similar trajectories, sometimes modelled by faster-converging exponential functions, have been identified in other studies25,27,30,102,103,104,105. Thus, the carbon stocks in an area of a grid cell x previously used to grow crop i were assumed to regenerate according to the function$$left{begin{array}{ll}{{C}}_{{{{{{rm{agricultural}}}}}}}(x)+sqrt{frac{t}{{{T}}_{{{{{{rm{carbon}}}}}}}(x)}}cdot ({{C}}_{{{{{{rm{potential}}}}}}}(x)-{{C}}_{{{{{{rm{agricultural}}}}}}}(x)) & {{{{{rm{if}}}}}},t ; < ; {{T}}_{{{{{{rm{carbon}}}}}}}\ hskip14.7pc{{C}}_{{{{{{rm{potential}}}}}}}(x) & {{{{{rm{if}}}}}},tge {{T}}_{{{{{{rm{carbon}}}}}}}end{array}right.$$ (16) where, using the same notation as further above$${{C}}_{{{{{{rm{potential}}}}}}}(x) ={{C}}_{{{{{{rm{potential}}}}}},{{{{{rm{vegetation}}}}}}}(x)+{{C}}_{{{{{{rm{potential}}}}}},{{{{{rm{SOC}}}}}}}(x)\ {{C}}_{{{{{{rm{agricultural}}}}}}}(x) ={{C}}_{i}(x)+0.75cdot {{C}}_{{{{{{rm{potential}}}}}},{{{{{rm{SOC}}}}}}}(x)$$ (17) Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Characterization of rice farming systems, production constraints and determinants of adoption of improved varieties by smallholder farmers of the Republic of Benin

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    Urbanization influences the distribution, enrichment, and ecological health risk of heavy metals in croplands

    General characteristics of study soilsTable 2 presents the descriptive statistics regarding the soil characteristics. Significant changes were observed in the distribution of sand (110–850 g kg−1), silt (50–530 g kg−1), clay (100–610 g kg−1), and soil textural class (7 texture classes) showing the diversity of natural and human processes involved in the formation and development of these soils28. Almost all soil samples were alkaline (with reaction at a range of 7.4–8.1) and calcareous (with CCE at a range of 5.5–35%). The EC of some soils was  > 4 dS/m (about 7% of the soil samples), indicating the partial salinity of the study soils. The organic carbon and total N contents of the soils were, on average, 2% (0.8–3.1%) and 0.28% (0.05–0.51%), respectively, placing them within the range of the moderate class. Likewise, the mean CEC of the soil, which is an effective indicator of soil fertility and quality, was in the moderate class of 12–25 cmol kg−129. The CEC was found to be highly correlated with clay (r = 0.76 P  Pb (58 mg kg−1)  > Ni (55.4 mg kg−1)  > Cu (38.8 mg kg−1)  > Cd (0.88 mg kg−1). In most soil samples, these ranges are comparable with data reported for other urban soils around the world—e.g. Ref.30 in Poland, Ref.31 in China, and Ref.32 in Greece. The values of Cd, Cu, and Zn were below their acceptable ranges as per the international standards4 in all soil samples. Nonetheless, the Pb and Ni contents were higher than their acceptable ranges in 13.1% and 17.4% of the samples, respectively. Furthermore, the concentrations of the five elements were higher than their background values in all urban soil samples. This difference was considerable for Cd, Pb, and Ni. The heavy metals had CV in the order of Cd (53%)  > Pb (51%)  > Ni (46%)  > Zn (21%)  > Cu (18%). This CV variation implies great variations in Cd, Pb, and Ni, which is linked to anthropogenic activities33. The background values of the metals, estimated by the median absolute deviation method10,14, were 52.3, 18.7, 0.45, 29.1, and 30.8 mg kg−1 for Zn, Cu, Cd, Pb, and Ni, respectively.We compared the concentrations of the heavy metals between urban and non-urban soils and found significant increases in the concentration of the metals in most soil types (Fig. 2). The urban soils had 17–36%, 14–21%, 41–70%, 43–69%, and 13–24% higher Zn, Cu, Cd, Pb, and Ni contents than the non-urban soils. The effluent and waste entry from multiple food processing and storage units, dying plants, metal plating facilities, and plastic production in close proximity of the study area is believed to be the reason for the high concentration of these trace elements. Research in various parts of the world, e.g., Ref.34 in India, Ref.35 in Brazil, and Ref.36 in China, has documented that the facilities have introduced significant quantities of heavy metals to soils. However, traffic and agrochemicals also play a key role in the accumulation of heavy metals in this region10.Figure 2The comparison of the mean values of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in metal content within each soil type at P  Ni  > Cu. These findings are comparable to the results reported by37 and12. The highest EF for all five elements was observed in the Fluvisols soil type, reflecting that this soil type had been exposed to element pollution induced by urban activities to a greater extent than the other soil types. In a study on the pollution potential of four soil types in Central Greece, Ref.38 reported different ranges of element pollution across different soil types.Figure 3The comparison of the mean enrichment factor of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in enrichment factor within each soil type at P  Pb (1.89)  > Ni (1.86)  > Cu (1.73)  > Zn (1.51). Mean PI for non-urban soils followed the order Cd (1.5)  > Zn (1.4)  > Cu (1.33)  > Pb (1.31)  > Ni (1.29). Nearly 7% and 16% of the urban soils showed moderate pollution (MP, PI = 2–3) and high pollution classes (HP, PI  > 3) of PI for Cd and 39% and 4% showed the MP and HP class of PI for Pb, respectively. However, the PI class was low pollution (PI = 1–2) for all soil samples and soil types in the non-urban soils. The results on the pollution index indicate a widespread intensification of soil pollution in urban soils across all studied heavy metals.Table 3 The level and terminology of PI and Ei of the analyzed heavy metals in urban and non-urban soils.Full size tableEcological risk, Ei was similarly found to be significantly higher in the urban soils than in the non-urban soils, even though the concentration of all elements except Cd fell within the low-risk class (Ei ≤ 40) in both urban and non-urban soils (Table 3). The mean Ei for Cd was 58.7 (moderate-risk class) and 39.2 (low-risk class) in the urban and non-urban soils, respectively. This means that urban activities have enhanced the ecological risk class of Cd by one grade. Overall, Cd had the highest EF, PI, and Ei among all heavy metals and in all soil samples, indicating a greater risk potential by Cd than Zn, Cu, Pb, and Ni across the water-soil–plant-human domain. Elevated Cd pollution by anthropogenic activities has been widely reported in the literature10,12,39. Cadmium as a Group 1 carcinogen element40 can accumulate in plant tissue without exhibiting visual symptoms. Therefore, Cd generally transfers from soil to the food chain covertly. Cadmium pollution can also influence soil quality and reduce crop yields and grain quality3.Similar to EF, PI, and Ei, the mean ER was significantly elevated in all urban soil types than the non-urban soils (Fig. 4). Among different soil types, the ER magnitude was in the order of Fluvisols (66.6%)  > Regosols (66.1%)  > Cambisols (59.8%)  > Calcisols (47%). These results indicate that Fluvisols carry a higher ecological risk potential for heavy metal accumulations than other soil types. In the study region, Fluvisols due to higher fertility and productivity are subject to more intense and extensive agronomic operations than other soil types13. Heavy application of agrochemicals (e.g., pesticides, herbicides, insecticides, and chemical fertilizers), accelerate the heavy metal input to the Fluvisols. Widespread application of nitrogen fertilizers and subsequent reduction in average soil pH markedly increases the solubility of certain heavy metals (e.g., Zn, Cu, Cd) which can be another factor increasing the ecological risk of heavy metal contamination in Fluvisols41. In addition, these Fluvisols are located on the margin of open urban wastewater channels, which are sometimes used for irrigation. A combination of mentioned processes can be implicated for higher ER of Fluvisols than that of other soil types as for BF, PI, and Ei.Figure 4The comparison of the mean ecological risk of selected heavy metals between urban and non-urban soils in different soil types. Different letters indicate significant differences in ecological risk within each soil type at P  Cu  > Ni  > Cd  > Pb in the roots, partially differing from that of the grain—Zn  > Cu  > Pb  > Ni  > Cd. Heavy metals concentrations observed in the corn roots and grains are almost comparable with those reported by42 in China and43 in Peru.Table 4 Summary statistical attributes of the concentration of heavy metals in corn root (R) and grain (G) along with their BCF and TF.Full size tableThe accumulation of heavy metals in the edible parts of corn is of higher importance. In the present study, the concentrations of these metals were lower than the acceptable level in the corn grains based on international references44. So, the consumption of corns grown in the regions should not threaten human and animal health in the short term, but caution should be exercised in their long-term consumption because some of these elements, especially Cd and Pb, which have long decomposition half-lives, gradually accumulate in body organs, especially in kidneys and livers45. Besides, the ratio of Zn, Cu, Cd, Pb, and Ni of the corn grain to their acceptable standard concentration, known as the pollution index of crop heavy metals, Ref.12 was lower than 0.7 for most corn samples, indicating the unpolluted risk class.The mean concentrations of Cd, Pb, and Ni were 5, 3.1, and 9.2 times as great in the corn roots as in their grains. This observation exhibits a notable phytoremediatory function of corn roots through restriction of radial translocation of heavy metals to the xylems and eventually into the grains. A similar trend of heavy metal accumulation in different plant organs has been reported in previous observations46,47. Based on Kabata-Pendias4 and Adriano22, plant cells can use the defensive tools of the roots to cope with heavy metals, especially Cd and Pb—highly toxic metals to plant cytosols. Accordingly, plant cells can fix these elements in the root system by such approaches as precipitating on cell walls, storing in vacuoles, and/or chelating by phytochelatins, thereby alleviating their toxic effects and inhibiting their translocation to plant shoots. For Zn, Cu, and Cd metals, a significant correlation was observed between their concentration in corn roots and grains. But, a less significant correlation (P  Cu (0.17)  > Zn (0.12)  > Ni (0.02)  > Pb (0.01). This implies that Cd, and to a smaller extent Cu is taken up by corn roots from the soil more readily, but Pb and Ni are less absorbable. These results are consistent with the reports of48 and46. The greater value of BCF-Cd may be related to a combination of the specific factors e,g., Cd concentration and chemistry, as well as soil characteristics (e.g., soil texture, pH, and calcium carbonate content)4. As was already discussed, the examined soils were characterized by high alkaline (pH = 7.4–8.1) and calcareous properties (CCE = 5.5–35%) with a high concentration of Soluble salts (EC = 0.7–6.6 dS m−1). These characteristics can result in the formation of complex Cd ions, especially CdOH+, CdCl20, CdCl+, CdSO40, and CdHCO3+4,22. These ions are plant-available, resulting in a further increase in Cd BCF. Regarding Ni and Pb, the alkaline and calcareous properties of the soils may have motivated insoluble compounds such as NiHCO3+ and NiCO30 (for Ni) and Pb(OH)2, PbCO3, PbSO4, and PbO (for Pb)4,22. These compounds cannot be uptake by plant roots, which may have resulted in a significant decrease in the BCF of these metals versus the other analyzed elements.Like BCF, the heavy metals had TF of  Pb (0.21)  > Cd (0.2)  > Ni (0.15). This implies that Zn and Cu are translocated from roots to grains readily, about four times as great as the other metals, while Ni, Cd, and Pb are translocated in smaller concentrations.The comparison of BCF and TF of Cd showed that less than 30% of Cd, on average, accumulated in the corn roots were translocated to the grains. This states that Cd is immobilized by various mechanisms before it can find its way into the grains. Some of the important mechanisms include (i) the antagonistic effects of Cd with other equivalent elements, especially Zn, Fe, and Ca, in the vascular system of corn, which reduces its mobility in the corn root-stem-grain system22, (ii) Cd sequestration in active exchange sites on the cell wall in the corn root-stem pathway10, and (iii) the binding of Cd with some specific compounds, e.g., phytochelatins of root vacuoles, which immobilizes it before its translocation to grains4,22. Lin and Aarts52 remarked that Cd mostly tends to be trapped in root vacuoles, which reduces its translocation to the upper parts of the plants. In general, it was found that corn plants have a high potential to absorb and accumulate Cd in their roots and Zn in their grains, which is consistent with previous studies41. For the majority of heavy metals, the values of BCF and TF in different soil types were in the order of Fluvisols  > Regosols  > Cambisols  > Calcisols, indicating that the great variety of soil types for the uptake and translocation of heavy metals in the soil-root-grain of the corn (Fig. 5).Figure 5Effect of soil type on the mean bioconcentration factor (a) and translocation factor (b) of selected heavy metals in urban soils. Different letters indicate significant differences in bioconcentration and translocation factors among soil types for each metal at P  Zn  > Cu  > Pb  > Ni for children, differing from that for adults (Cu  > Cd  > Pb  > Zn  > Ni). The values of HQ was  1 in over 87% of the samples, implying the low non-carcinogenic risk of this metal for corn-consuming children in the study region53. Rapidly developing children’s nervous system are highly sensitive to environmental factors, including heavy metals, so even a relatively low concentration of Cd in children’s blood may irreversibly affect their mental growth and functioning54.The highest HI was observed in children (min = 1.16, max = 2.31, mean = 1.63) followed by women and men which was similar to the found pattern of HQ (Table 7). These data show a moderate non-carcinogenic health risk (1 ≤ HI  More

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    Study on the risk of soil heavy metal pollution in typical developed cities in eastern China

    Characteristics of heavy metal concentrationsOn the basis of the soil sample collection and chemical analysis, the concentration data for heavy metals in the urban soils of Wuxi were obtained. Through the statistical analysis of the soil heavy metal concentration data (Table 1), on the whole, the concentration of each heavy metal is as follows: Mn  > Zn  > Cr  > Ni  > Pb  > Cu  > Co  > Be  > Cd. Among these, the concentration range of Cr was 64.5–99 mg kg-1, and the average concentration was 72.9 mg kg−1. The concentration range of Ni was 31.4–67.5 mg kg−1, and the average concentration was 38.2 mg kg−1. The concentration range of Cu was 19.8–37.2 mg kg−1, and the average concentration was 25.5 mg kg−1. The concentration range of Zn was 72.4–1146 mg kg−1, and the average concentration was 90.2 mg kg−1. The concentration range of Cd was 0.34–1.06 mg kg−1, and the average concentration was 0.51 mg kg−1. The concentration range of Pb was 25.6–66.4 mg kg−1, and the average concentration was 37.6 mg kg−1. The variation coefficients of urban soil heavy metal concentration in Wuxi is between 0.09 and 0.33, which is less than 1. The spatial fluctuation of urban soil heavy metal concentration in Wuxi is small, indicating that the sources may be the same or similar.Table 1 Statistics of the heavy metal concentrations and Pb isotope ratios in the urban soils of Wuxi city (unit of heavy metal: mg kg−1; CV: coefficient of variation).Full size tableBy analysing the spatial distributions of the urban soil heavy metal concentrations in Wuxi, several obvious spatial distribution characteristics are found (Fig. 2). First, the heavy metals have high values in the central area of Wuxi, due to where has a high population density and various industries. The central aggregation of Pb is more obvious. Due to the dense roads in the city centre, vehicle traffic, bus stop signs and gas stations are mostly concentrated here, which will lead to Pb contents in this area that are significantly higher than those in other areas. In addition to the heavy metal concentrations, such as those for Cu, Zn and Cr in the downtown area, there are also areas with high values in western Wuxi and low values in eastern Wuxi. This phenomenon may be related to the land use types in Wuxi. In the western area of Wuxi, most land use types are urban and construction land, and the soils in this area are greatly disturbed by human activities. In the eastern region of Wuxi, woodland and grassland account for a large proportion of the land use types, which are less disturbed by human activities.Figure 2Spatial distribution characteristics of heavy metals in the urban soils of Wuxi city (unit: mg kg−1) [the figure was generated by Yan Li using the ArcGIS 10.2 (http:// https://developers.arcgis.com/)].Full size imageSource analysis of heavy metalsExploring for heavy metal pollution from emission sources is an important prerequisite for the study of urban soil pollution. By analysing the sources of heavy metals in soil environments, we can accurately determine which industries are major sources28,29,30 and whether there is homologous pollution. This is not only a theoretical basis for the study of lake sediment pollution and to clarify the risks brought by different pollution sources to the urban soil environment but also provides important guides for local government control of specific polluting industries and pollutant emissions. Based on this, the correlations and significance of heavy metals in the urban soils of Wuxi were analysed (Table 2). Generally, a heavy metal pollution source will emit multiple heavy metals at the same time. If the pollution source has a large emission, the concentration of these heavy metals in the environment will show a high level; on the contrary, if the emission of this pollution source is small, the concentration of these heavy metals in the environment will show a low level10. The correlations between the heavy metals Zn, Cr, Ni, Pb, Cu and Cd are between 0.655–0.907 and show strong correlations and significance at a level of 0.01. The strong significant correlations between different heavy metals indicate that these heavy metals have similar emission sources and transmission routes, which also means that they have consistent sources.Table 2 Correlations of Heavy Metals in the Urban Soils of Wuxi City.Full size tableTo further determine which industries are the sources of the heavy metals found in the urban soil of Wuxi, we analysed the Pb isotope data. The variation range of 208Pb/206Pb in soil is 2.09–2.12, and the average value is 2.10. The variation range of 206Pb/207Pb in soil is 1.17–1.18, and the average value is 1.177 (Table 1). After consulting relevant literature and materials, the main pollution sources of heavy metals in cities in eastern China include coal combustion, oil combustion, factory emissions, municipal wastes and so on3. Therefore, we collected the corresponding Pb isotope data in the emissions of heavy metal pollution sources. By collecting and comparatively analysing the Pb isotope data of known pollution sources (Fig. 3), it was determined that the Pb isotopes of the urban soil heavy metals in the soils of Wuxi have distinct characteristics. First, the Pb isotope distributions in the soils of Wuxi are relatively concentrated, and the ranges of variation are relatively small, which indicate that these heavy metals may have the same source or similar sets of sources. Second, the Pb isotopes in the urban soils of Wuxi city have few similarities with those of the uncontaminated soils and granites in eastern China; in contrast, the Pb isotopes in the urban soils of Wuxi are distributed in areas that are associated with coal combustion, automobile exhaust and urban waste (supplementary materials). The urban soil heavy metals in Wuxi generally have similar pollution sources and are greatly affected by human activities such as coal combustion and automobile exhaust emissions. Wuxi has a developed industrial economy and large numbers of factories. In the production and processing activities, the combustion of energy and fuel and the incomplete utilization of raw materials will lead to the enrichment of pollutants in the surrounding environment. By comparing other studies30,31, the Pb isotope analysis results in this study well indicate the source of soil heavy metals in Wuxi and make up for the Pb isotope data in this area. In the process of urban development, we should develop and apply clean energy, reduce the utilization of petroleum fossil fuels, and control the enrichment of heavy metals and other pollutants in the soil from the source.Figure 3Comparison of the Pb isotope compositions in the urban soils of Wuxi city with known sources.Full size imageEcological risk analysisBy calculating the potential ecological risk index for the heavy metals in the urban soils of Wuxi, the risks of heavy metals in the Wuxi soils were evaluated (Table 3). According to previous studies21, an Ei value lower than 40 indicates that a heavy metal is in a low-risk state at this location, and Ei values greater than or equal to 40 indicate that a heavy metal represents a high-risk state at this location. The average value of the potential ecological risk index of soil heavy metal Cd in Wuxi is 80.3, which represents a high-risk state. The average distributions of the potential ecological risk indexes of the heavy metals Cr, Cu, Zn, Pb and Ni are 1.8, 4.3, 1.1, 5.5 and 4.8, respectively, which all indicate a low-risk state. The risk statuses of different heavy metals may show certain correlations in space, which may be mutually complementary or antagonistic. Examining the spatial interactions of different heavy metal compound pollutants in urban soils plays an important role in the prevention and control of urban heavy metal pollution. Based on this, we used the Lisa analysis method to explore the spatial correlations of the different heavy metal risks in the urban soils of Wuxi (Fig. 4). The Moran scatter diagram can be divided into four quadrants that correspond to four different spatial patterns. High means that the variable value is higher than the average value, and Low means that the variable value is lower than the average value. In the upper right quadrant (High–High), a high-value area is surrounded by high-value neighbours; in the upper left quadrant (Low–High), a low-value area is surrounded by high-value neighbours; in the lower left quadrant (LL), a low-value area is surrounded by low-value neighbours; and in the lower right quadrant (High–Low), a high-value area is surrounded by low-value neighbours. High-High and Low-Low indicate that the differences between the region and its surrounding areas are small; that is, the regions with higher or lower values are concentrated, while the Low–High and High–Low quadrants indicate that the variable values between a region and its surrounding areas are different to a certain extent.Table 3 Ecological risk and health risk analysis of heavy metals in the urban soils of Wuxi (Cr-E represents the ecological risk of metal element Cr; Ni-E represents the ecological risk of metal element Ni; Cu-E represents the ecological risk of metal element Cu; Zn-E represents the ecological risk of metal element Zn; Cd-E represents the ecological risk of metal element Cd; Pb-E represents the ecological risk of metal element Pb; ADDderm-C is the average exposure to skin contact pathways for child; ADDderm-A is the average exposure to skin contact pathways for adult; ADDing-C is the average daily exposure to intake pathway for child; ADDing-A is the average daily exposure to intake pathway for adult; HI-C is the total health risk caused by accumulation of heavy metals in multiple ways in the same environmental medium for child; HI-A is the total health risk caused by accumulation of heavy metals in multiple ways in the same environmental medium for adult).Full size tableFigure 4LISA analysis of the ecological risks from different heavy metals [the figure was generated by Yan Li using the ArcGIS 10.2 (http:// https://developers.arcgis.com/)].Full size imageIn this study, two main results were obtained from spatial correlation Lisa analysis between different heavy metals. One is a High-High area, which is mainly distributed in the central and western regions of Wuxi city, which is consistent with the spatial distribution of the urban soil heavy metal concentrations in Wuxi city and is strongly disturbed by human activities. The other is the insignificant area, in which there are also large numbers of factories and enterprises and in which the forestland and grassland are distributed at intervals, which leads to an insignificant spatial correlation of soil heavy metal contents. Based on the above analysis, the high-risk areas for heavy metals in the urban soils of Wuxi are mainly concentrated in the central and western regions, and the relevant management activities need to be given great attention. In the eastern region, sporadic high-risk areas are also present, which should also receive due attention. Moran’s I is a method to measure the interdependence and degree of objects or phenomena by constructing statistics on certain characteristics or attributes for a certain spatial unit in the study area and the surrounding spatial units. It can be used to describe the spatial characteristics of spatial units such as aggregation or outliers in the distribution of certain attributes and is a very important technology in spatial data analysis33,34. However, few studies have applied it to the spatial relationship analysis of different heavy metals in urban soil.Health risk analysisBy using the health risk assessment model that is recommended by the U.S. EPA, this study calculated the health risks of soil heavy metals to adults and children through skin contact and ingestion. For both adults and children, the risk of soil heavy metals through ingestion was much higher than that caused by skin exposure (Table 3). For children, the total health risk that was caused by soil heavy metals is 0.078, which is four times that of adults. This may be related to children’s habits. Most children like to play with sand and climb around on the ground. These behaviours greatly increase the frequency of children contacting the soil, which thus increases the health risk caused by heavy metals in the soil. To further explore the spatial characteristics of the health risks of heavy metals in the soils of Wuxi, this study provides spatial predictions of the health risk values of soil heavy metals (Fig. 5). The total health risk values of soil heavy metals for children and adults have similar spatial distribution characteristics. High health risk values appear in the central area of Wuxi and decrease in a ring-shaped pattern. This is similar to the development degree of the city. The downtown area of Wuxi is densely populated, the pedestrian flow is very large, and the health risk of soil heavy metals in this area is very high, which poses a very serious potential threat. The health risk values for the western region of Wuxi are high, and there is also a potential threat. When compared with western Wuxi, eastern Wuxi has a lower risk.Figure 5Health risk analysis of heavy metals in the urban soils of Wuxi [the figure was generated by Yan Li using the ArcGIS 10.2 (http:// https://developers.arcgis.com/)].Full size image More

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