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Air pollution remains a key environmental problem in an increasingly urbanized world. To quantify health impacts and support informed policies, the population exposure needs to be accurately monitored. However, the inherent spatial variability of air quality poses a tenacious challenge to this. While concentrations of traffic-related pollutants like nitrogen dioxide (NO2) are known to vary over short distances, official monitoring networks remain inherently sparse, as reference stations are costly to construct and operate. Here we show that citizen science provides an cost-effective way to collect large, spatially distributed datasets that critically complement official monitoring.

Additional file 1: Table S1. Survey questionnaire for collection of malariometric and risk factor data. Table S2. Summary characteristics from the population census of the three villages divided into the sampled and not sampled individuals for plasmodium prevalence identification. Table S3. Single and mixed individual plasmodium infections as detected by PCR for the three village. Pf: Plasmodium falciparum, Pv: Plasmodium vivax, Pm: Plasmodium malariae. PfPv, PfPm, PvPm and PfPvPm stands for the mixed infections occurring in one individual for multiple plasmodium species. Numbers are counts of infected individuals. Table S4. Results of univariate analysis comparing risk factors for infection with Plasmodium spp. (all species combined), P. falciparum, P. vivax and P. malariae. Odds ratio and upper and lower 95% confidence limits were calculated with respect to the reference category, which is the first reported subgroup.

Additional file 8. Spatial clusters of falciparum malaria cases. Clusters of villages with higher number of malaria cases detected from 2010 to 2014 in Ratanakiri Province. Each numbered circle or dot represents a significant cluster. The un-numbered circles or dots are not significant clusters. Incidence of falciparum malaria cases is given per commune.

Additional file 6. Annual number of confirmed vivax malaria cases by village and vivax malaria incidence rate per 1000 inhabitants by commune. Although several villages did not report in 2010 and 2011 (37 and 28%, respectively), the number of confirmed malaria cases and incidence decreased steadily from 2012 to 2014.

Additional file 5. Annual number of confirmed falciparum malaria cases by village and falciparum malaria incidence rate per 1000 inhabitants by commune. Although several villages did not report in 2010 and 2011 (37 and 28%, respectively), the number of confirmed malaria cases and incidence decreased steadily from 2012 to 2014.

Additional file 10. Spatial clusters of vivax malaria cases. Clusters of villages with higher number of malaria cases detected from 2010 to 2014 in Ratanakiri Province. Each numbered circle or dot represents a significant cluster. The un-numbered circles or dots are not significant clusters. Incidence of vivax cases is given per commune.

Additional file 7. Spatial clusters of villages with significantly higher risk of all malaria cases (all Plasmodium species combined) from 2010 to 2014 in Ratanakiri Province. Only significant clusters are showed. RR: Relative risk. LLR: Log likelihood ratio.

Additional file 9. Spatial clusters of villages with significantly higher risk of falciparum malaria cases from 2010 to 2014 in Ratanakiri Province. Only significant clusters are showed. RR: Relative risk. LLR: Log likelihood ratio.

Additional file 4. Annual incidence rates per 1000 inhabitants for all reporting villages. The total number of reporting villages was 277 including main- and annex-villages, but not all of them reported every year.

Additional file 1. Form for distribution of repellent bottles and recovery of empty bottles or household data sheet. This sheet was used to collect information from household representative during two-weekly bottle exchange. Each sheet is for each bottle exchange and for a household. Each household had a unique identification code (family code) which was used during the entire project.

Additional file 7. Univariate analysis of potential determinants for consumption. The table shows all results of univariate analysis looking for relationship between each of ten potential determinants and average two-weekly repellent consumption in 2012 and 2013.

Additional file 3. Questionnaire for distributors for socio-economic status and repellents distribution survey. A Khmer version of this questionnaire was used during the distributor survey to collect information on socio-economic status of all distributors and how they distribute repellents.

Additional file 4. Characteristics of households included in the Household Survey. The table summarizes potential variables to be included in the principal component analysis for socio-economic status which is possible to influence on distributor-household contact and repellent consumption.

Correlation between monthly survival probability and maximum antibody prevalence measured per year (2010 until 2017). (DOCX 84 kb)

Correlation between monthly survival probability and maximum antibody prevalence measured per year (2010 until 2017). (DOCX 84 kb)