Philippa Kaur

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Data collection and potential selection bias
We conducted two joint surveys from July to September 2017 in Kabwe: the Kabwe Household Socioeconomic Survey (KHSS) 2017 conducted by the Central Statistical Office of Zambia and University of Zambia under the supervision of the authors, and a BLL survey performed by the authors. The surveys were approved by the University of Zambia Research Ethics Committee (UNZAREC; REF. No. 012-04-16). Further approvals were granted by the Ministry of Health through the Zambia National Health Research Ethics Board and the Kabwe District Medical Office. The data were collected in accordance to the Declaration of Helsinki, and the informed consent was obtained from all the study participants including the parents/legal guardian of the minor subjects for participating in the study.
The two surveys were designed consistently and targeted the same sample households selected in the following two-step approach. In the first step, utilising the Zambia’s national census frame which divides the Kabwe district into 384 standard enumeration areas (SEAs), we randomly selected 40 SEAs across the entire district. In the second step, we randomly selected 25 households (and a few replacements) from each sampled SEA. The sampling weights were generated to account for population differences across the SEAs.
The KHSS 2017 conducted interviews with 895 households (4,900 individuals) at houses and collected data on socioeconomic, demographic and geographic information. The response rate was 88.2%, and we could regard the data adjusted by the sampling weights as representative of the entire Kabwe population (for more details of the survey, see the report33).
To obtain BLL data, we conducted a blood sampling survey concurrently with the KHSS 2017. For hygiene and ethical considerations, we selected 13 local clinics to perform the blood sampling, instead of collecting blood at houses. We invited up to four members (two children aged 10 years or younger and their parents or guardians) from each sample household for the blood sampling. We prioritised young children over children older than 10 years old. The invitations were made sequentially. We assigned identical venues and dates for households from the same SEAs. The typical assigned dates had a 3-day window from the day after the invitation. However, we allowed for some flexibility and sampled the blood of those who visited the clinic even after the assigned time window, as long as the clinic was operational for households from other SEAs. Therefore, the window for blood sampling was effectively the number of days from the day after the invitation until the pre-set blood sampling period in each clinic was over, which had a substantial variation across households from 3 days to a month. We revisit this feature of the survey window when setting up our econometric model later. A total of 372 households (41.6%) participated in the blood sampling and, on average, 2.2 members from the participating households provided blood samples.
We performed blood digestion and metal extraction as described by our previous study34 with minor modifications and measured BLLs using an Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). In addition, we also measured BLLs with a portable analyser, LeadCare II, to obtain quick results22. However, we in this study focus on the ICP-MS data, considering their general accuracy. See the Supplementary Material Section S1 for details on the methods used to measure BLLs and the difference in the data between the two analysers.
Regardless of the accuracy of the techniques, however, we further need to account for the risk of selection bias in the BLL data. In the absence of formal and compulsory testing mechanisms, we relied on individuals’ voluntary (self-selected) visits to the clinics. However, the participants in blood sampling could have traits leading to higher or lower BLLs than the population. Such traits can include education, gender, age and living standards. The survey design did not prioritise children aged 11 years or older, and this could also contribute to the deviation of characteristics, although a small number of such children attended clinics. Moreover, certain unobservable characteristics affecting BLLs can further differ between the participants and non-participants. For example, those with greater preferences for health possibly had low BLLs but tended to participate in the blood sampling surveys, whereas those with a high innate physiological capacity for lead excretion possibly tended not to participate because they had low BLLs and did not perceive symptoms of lead poisoning. These issues can lead to selection bias, and the raw data observed from the voluntary participants can fail to illustrate the lead poisoning conditions of the population.
BLL estimation approach
To correct for potential selection bias, we first estimated the equations to explain BLLs of children aged 0–10 years and adults aged 19 years or above. Then, using the estimated equations, we calculated BLLs for all individuals, including children aged 11–18 years and those in the other age groups who did not participate in the blood sampling.
BLLs generally depend on the ambient pollution level, the opportunities of exposure to pollution, the physiological capacity of lead absorption and excretion, and the knowledge and technologies used to prevent lead poisoning. We controlled for ambient pollution levels by including the distance, direction, and altitude of household location—the first two variables are with respect to the mine waste dumping site (Black Mountain). The remaining factors were measured by age and various other individual and household characteristics denoted by ({{varvec{X}}}_{i}). Data for these variables are available regardless of participation in blood sampling. We assumed the following equation for BLL:

$$begin{aligned} log BLL_{i} & = beta_{dis} log distance_{i} + beta_{dir1} direction_{i} + beta_{dir2} direction_{i}^{2} \ & quad + beta_{alt} altitude_{i} + fleft( {age_{i} } right) + {varvec{X}}_{i} user2{gamma^{prime}} + varepsilon_{i}. hfill \ end{aligned}$$
(1)

The logarithmic form for BLL adjusts its distribution to approximately normal—BLL is bounded from below and has a skewed distribution—and allows the factors on the right-hand side to have proportional effects rather than level effects. ({varepsilon }_{i}) is the independent and identically distributed error term that captures noise, such as casual fluctuations and measurement errors in BLLs, and the effects of unobservable factors. While we presented a single equation above, we assumed different equations for children aged 0–10 years and adults aged 19 years or above.
Below, we discuss our specification in detail.
Geographic factors
Existing studies have examined the relationship between the geographic location and ambient pollution level12,13,14. Since lead is transported from the mine waste dumping site through the flow of wind and water, the distance from the site is negatively correlated with ambient lead levels. The soil lead contamination spreads to the western side of the site, particularly towards the west-northwest (WNW), which corresponds to the direction of the prevailing local wind. The contamination also slightly extends to the low-elevation south-eastern side, reflecting pollution transported by water. The northern and southern sides are the least contaminated.
We defined (distanc{e}_{i}) as the distance between the mine waste dumping site and the location of (i)’s household, with ({beta }_{dis} More

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Field N2O flux measurements
At seven different semi-arid and arid savanna regions in Kenya (Fig. 1, Supplementary Fig. 9), N2O measurements from soils at 46 boma sites and 22 adjacent reference sites (undisturbed savanna) were carried out. At each boma and control site, 3–7 plots were chosen randomly for flux measurements. Local members of the pastoral communities, including herders and/or community elders were interviewed for information on the time since abandonment of each boma.
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