Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60–69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.

Organochlorine pesticides (OCPs) and stable isotopes were measured in muscle from 4 bird and 5 fish species from the Ethiopian Rift Valley region where DDT is used for malaria control and vast agricultural activities are carried out. We investigated the bioaccumulation of OCPs such as DDTs, HCHs, chlordanes, and heptachlors between the species, and examined the potential risk posed by these compounds for bird species. Significant differences in contaminant profiles and levels were observed within the species. Levels of total OCPs ranged from 3.7 to 148.7 μg/g lipid in bird and 0.04 to 10.9 μg/g lipid in fish species. DDTs were the predominant contaminant, and a positive relationship between δ15N and ΣDDT concentrations was found. The main DDT metabolite, p,p′-DDE was the most abundant and significantly greater concentrations in bird species (up to 138.5 μg/g lipid), which could have deleterious effects on survival and/or reproduction of birds.

This systematic review and meta-analysis aimed to investigate the association between air pollution and DNA methylation in adults from published observational studies. PubMed, Web of Science and Embase databases were systematically searched for available studies on the association between air pollution and DNA methylation published up to March 9, 2021. Three DNA methylation approaches were considered: global methylation, candidate-gene, and epigenome-wide association studies (EWAS). Meta-analysis was used to summarize the combined estimates for the association between air pollutants and global DNA methylation levels. Heterogeneity was assessed with the Cochran Q test and quantified with the I² statistic. In total, 38 articles were included in this study: 16 using global methylation, 18 using candidate genes, and 11 using EWAS, with 7 studies using more than one approach. Meta-analysis revealed an imprecise but inverse association between exposure to PM₂.₅ and global DNA methylation (for each 10-μg/m³ PM₂.₅, combined estimate: 0.39; 95% confidence interval: 0.97 - 0.19). The candidate-gene results were consistent for the ERCC3 and SOX2 genes, suggesting hypermethylation in ERCC3 associated with benzene and that in SOX2 associated with PM₂.₅ exposure. EWAS identified 201 CpG sites and 148 differentially methylated regions that showed differential methylation associated with air pollution. Among the 307 genes investigated in 11 EWAS, a locus in nucleoredoxin gene was found to be positively associated with PM₂.₅ in two studies. Current meta-analysis indicates that PM₂.₅ is imprecisely and inversely associated with DNA methylation. The candidate-gene results consistently suggest hypermethylation in ERCC3 associated with benzene exposure and that in SOX2 associated with PM₂.₅ exposure. The Kyoto Encyclopedia of Genes and Genomes (KEGG) network analyses revealed that these genes were associated with African trypanosomiasis, Malaria, Antifolate resistance, Graft-versus-host disease, and so on. More evidence is needed to clarify the association between air pollution and DNA methylation.

The organohalogenated contaminants (OCs) including 12 organochlorine pesticides (OCPs), 7 indicator polychlorinated biphenyls (PCBs) and 7 polybrominated diphenyl ethers (PBDEs) were determined in soils collected from Kenya, Eastern Africa. The total OCPs fell in the range of n.d–49.74 μg kg⁻¹ dry weight (dw), which was dominated by DDTs and endosulfan. Identification of pollution sources indicated new input of DDTs for malaria control in Kenya. The total PCBs ranged from n.d. to 55.49 μg kg⁻¹ dw, dominated by penta- and hexa-PCBs, probably associated with the leakage of obsolete transformer oil. The soils were less contaminated by PBDEs, ranging from 0.19 to 35.64 μg kg⁻¹ dw. The predominant PBDE congeners were penta-, tri- or tetra-BDEs, varying among different sampling sites. Risk assessment indicated potential human health risks posed by OCs in soils from Kenya, with PCBs as the most contributing pollutants. The local authorities are recommended to make best efforts on management of OC pollution, particularly from DDTs and PCBs to meet the requirement of Stockholm Convention.

We investigated presence and levels of DDT in 163 breast milk samples from four South African villages where, in three of them, malaria is controlled with DDT-sprayed indoors. Mean ΣDDT levels in breast milk were 18, 11, and 9.5 mg/kg mf (milk fat) from the three DDT-sprayed villages, respectively, including the highest ΣDDT level ever reported for breast milk from South Africa (140 mg/kg mf). Understanding the causes for these differences would be informative for exposure reduction intervention. The Provisional Tolerable Daily Intake (PTDI) for DDT by infants, and the Maximum Residue Limit (MRL) were significantly exceeded. DDT had no effect on duration of lactation. There were indications (not significant) from DDT-sprayed villages that first-born female infants drink milk with more ΣDDT than first-born male infants, and vice versa for multipara male and female infants, suggesting gender involvement on levels of DDT in breast milk – requiring further investigation.

Inhabitants of dwellings treated with DDT for indoor residual spraying show high DDT levels in blood and breast milk. This is of concern since mothers transfer lipid-soluble contaminants such as DDT via breastfeeding to their children. Focusing on DDT use in South Africa, we employ a pharmacokinetic model to estimate DDT levels in human lipid tissue over the lifetime of an individual to determine the amount of DDT transferred to children during breastfeeding, and to identify the dominant DDT uptake routes. In particular, the effects of breastfeeding duration, parity, and mother's age on DDT concentrations of mother and infant are investigated. Model results show that primiparous mothers have greater DDT concentrations than multiparous mothers, which causes higher DDT exposure of first-born children. DDT in the body mainly originates from diet. Generally, our modeled DDT levels reproduce levels found in South African biomonitoring data within a factor of 3.

The iSimangaliso Wetland Park World Heritage site, located on the east coast of South Africa, spans ∼3300 km2 and constitutes the largest protected estuarine environment for hippopotami, crocodiles and aquatic birds in Africa. Given the ecological importance of this site and continued use of organochlorine pesticides (OCPs) in the region, this study focused on the nature, distribution and potential sources of organochlorine contamination within iSimangaliso Wetland Park. OCPs were widely distributed in surface sediment samples obtained from the four main Ramsar wetland systems within the park (Lake St Lucia, Mkhuze, Lake Sibaya and Kosi Bay). ∑HCH and ∑DDT were the dominant contaminants detected with concentrations in the range of 26.29–282.5 ng/g and 34.49–262.4 ng/g, respectively. ∑DDT concentrations revealed a distinctive gradient, with significantly higher concentrations at Kosi Bay and Lake Sibaya attributed to the application of DDT for malaria control. p,p'-DDE and p,p'-DDD were the dominant isomers detected, but the detection of p,p'-DDT in a number of samples reflects recent inputs of technical DDT. Highest concentrations of HCH, endosulfan and heptachlor were detected in sediments from Mkhuze and reflect the substantial residue load these wetlands receive from agricultural activities within the catchment area. Isomeric compositions indicate that endosulfan and heptachlor residues are derived mainly from historical application, while inputs of HCH, aldrin and endrin could be attributed to more recent usage at several sites. OCP sediment concentrations from iSimangaliso represent the highest yet recorded in South Africa and some of the highest reported globally this century. Sediments found within the lakes and wetlands of iSimangaliso represent large reservoirs of contaminants that pose ecotoxicological threats to this globally important biodiversity hotspot. Detailed investigation into the bioaccumulation and toxicological risks of OCPs within the wetland park is urgently required.

Polyethylene pellets provide a convenient means to monitor Persistent Organic Pollutants (POPs) in marine systems. Pellets collected between 1984 and 2008 at three South African beaches were analysed for PCB, HCH and DDT. Concentrations of all three POPs decreased over the last two decades, although this signal was less clear for PCBs, and further monitoring is needed to assess trends in this family of compounds. DDT concentrations at two sites were higher than previous records for southern Africa, but there is no evidence of a link to the ongoing use of DDT for malaria control. HCHs concentrations were lower than in pellets from the east coast of southern Africa, suggesting that this pesticide was mainly used in the eastern part of the region. Our study demonstrates the potential for International Pellet Watch to track temporal as well as geographical patterns in the abundance of POPs in marine environments.

Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.