Environmental Health Risks in Residential Settings: A Systematic Review of Indoor Air Pollution, Domestic Chemical Exposure, and Global Representation Inequities

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Materials and Methods A PRISMA-guided Systematic Literature Review (SLR) synthesized 44 Scopus-indexed studies (Jan 2015–Mar 2025). We charted exposure types, research designs, populations, variables, and geography. Results Indoor air pollution was most frequent (25/44; 56.8%), followed by soil/dust (10/44; 22.7%), chemical exposure (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%). Studies were concentrated in Asia (68.2%) and Africa (15.9%); Latin America & the Caribbean, as well as Oceania, each accounted for 2.3%. Designs were dominated by experimental/field–lab studies (40.9%), with cohort/longitudinal studies being rare (4.5%). Populations most examined were urban residents (63.6%); vulnerable groups included children (22.7%), older adults (6.8%), and low-income families (6.8%). The most-analyzed variables were heavy metal concentration (25.0%) and air quality (22.7%). Conclusion The residential risk burden is primarily linked to the use of solid fuels, poor ventilation, and household chemical practices, with apparent geographic and methodological gaps. Priorities include clean-fuel transition, ventilation upgrades, targeted risk communication on chemicals, and broader community-based research—framed within the One Health framework to integrate health, environment, and social actions. residential exposure indoor air pollution dust/soil heavy metals vulnerable populations PRISMA One Health Figures Figure 1 Figure 2 Introduction The intersection of climate change, urbanization, and domestic chemical exposure underscores the urgency of understanding environmental risks within residential contexts. Empirical evidence shows that indoor air quality (IAQ) can be significantly worse than outdoor air, with pollutant concentrations inside homes reported to be two to five times higher than those outdoors 1 , 2 . Key sources of indoor air pollution include emissions from cooking, volatile organic compounds (VOCs) from building materials, and pesticide residues. Climate change further exacerbates this issue by affecting outdoor air quality and intensifying urban heat island effects. Rapid urbanization has particularly adverse effects on low-income communities, which are more susceptible to indoor air pollution due to overcrowded living conditions, inadequate ventilation, and high-pollution household practices 3 . Additional exposure risks arise from proximity to traffic, industrial zones, and indoor heating and cooking systems 4 . Household chemicals, including VOCs from cleaning agents and paints, add to the complexity of indoor pollution. While many studies focus on individual pollutants, they overlook their cumulative effects. Thus, an integrated approach is required to assess the various sources of indoor contamination and their interactions 5 . Lifestyle, behavior, and cultural norms significantly influence exposure levels, and interventions' effectiveness—such as indoor plants or ventilation systems—depend largely on regional practices and preferences 6 , 7 . Geographic and climatic differences also play a crucial role in determining the success of mitigation strategies 8 . Moreover, geographic disparities in IAQ research reveal methodological bias. Most studies have focused on high-income countries, neglecting developing nations' distinct environmental challenges 9 . Green technologies effective in developed urban areas may not be feasible in regions with limited infrastructure 10 . Additionally, environmental events such as wildfires have worsened indoor air conditions by increasing the infiltration of smoke, particulate matter, and polycyclic aromatic hydrocarbons (PAHs) into residential spaces 11 . Climate-induced lifestyle stressors further intensify indoor pollution dynamics. Addressing indoor air quality in residential environments requires a multidimensional strategy that includes technological innovation, education, and regulatory reform. Since individuals spend most of their time indoors, public policies should prioritize improving indoor environmental quality, especially for vulnerable populations 12 . Technological innovations such as bioremediation and advanced filtration systems must be tailored to local cultural and socioeconomic contexts. The interplay between climate, urbanization, and household chemicals necessitates a comprehensive framework for improving the quality of residential air environments. Collaborative efforts are needed to tackle socioeconomic disparities and foster healthier living conditions. This study aims to (1) Identify dominant trends and thematic patterns in the literature concerning environmental health risks in residential settings; (2) Classify types of pollution, research methods, affected populations, and frequently studied variables; (3) Analyze methodological and geographic gaps that hinder the development of globally relevant policies and (4) Offer policy and research recommendations through a cross-sectoral One Health integration approach. This study offers novelty on two levels: content and approach. In terms of content, we not only map domestic environmental exposures (indoor air, dust/soil, water, and household chemicals), but also quantify the geographical representation gap in global research that has implications for policy equity. In terms of approach, we developed an antecedent–mediator–consequence network that integrates various key variables and guides the determination of intervention entry points. The combination of both produces One Health recommendations based on patterns of findings—prioritizing clean fuel transition, ventilation improvements, and chemical education, while also encouraging the expansion of study coverage and participatory study design to close the evidence gap across regions. Materials and Methods This study is a Systematic Literature Review (SLR) conducted to identify, classify, and synthesize scientific evidence related to health risks resulting from environmental exposure in residential areas. The review was structured by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines 13 , 14 1. Study Design and Theoretical Framework This SLR is not purely descriptive; it also explores the relationships among variables within a framework of antecedents (causes), mediators (intervening factors), and consequences (outcomes) of environmental health risks. To support this, a concept matrix and hypothesis network analysis were employed to map findings and identify significant research gaps. The analytical framework refers to Miles' taxonomy of research gaps 15 , developed from the model by Robinson et al. 16 and further refined by Müller-Bloch and Kranz 17 . Literature synthesis followed Cooper's taxonomy of literature reviews 18 , which emphasizes a neutral approach and organizes information within a historical and conceptual context without directly critiquing content. 2. Data Sources and Search Strategy Scientific literature was gathered from the Scopus database and selected for its comprehensive coverage of reputable academic publications. The keywords used in the search process included: "residential health risk," "environmental health risk," "household," and "indoor pollution," combined using logical operators (AND/OR). The search was limited to journal articles published between January 2015 and March 2025, written in English, and available in full-text format. 3. Article Selection and Exclusion The article selection process followed the four standard PRISMA phases: identification, screening, eligibility assessment, and inclusion. Of the initial 162 articles identified, 53 were excluded for not meeting technical or substantive criteria. One hundred nine articles were screened based on titles and abstracts, but 61 were inaccessible in full text. Finally, 48 articles underwent a complete eligibility assessment, and 44 were included in the final review. The entire selection process is illustrated in Fig. 1 (PRISMA Diagram). To ensure the validity and reproducibility of the selection process, the PRISMA diagram was created using Watase Uake Tools, a PRISMA 2020–compliant reporting software that facilitates a systematic, concise, and auditable visualization of the selection flow. Figure 1 . PRISMA Diagram of the Article Selection Process 4. Data Extraction and Synthesis The selected articles were analyzed using a thematic matrix that included the components of pollution, pollution-generating activities, affected population groups, primary study variables, methodological design, study location, and journal quartile rankings (Scimago Q1–Q4). The synthesis process was conducted descriptively and conceptually to identify patterns in study distribution, emerging thematic trends, and knowledge gaps, which may serve as a foundation for shaping future research directions. Results The following section presents a compilation of 44 international studies investigating environmental health risks in residential settings. These studies span diverse geographic regions and research methodologies, providing insights into pollution exposure pathways, vulnerable populations, and analytical approaches. Table 1 . International Research Database on Environmental Health Risks in Residential Areas Table 1 International Research Database on Environmental Health Risks in Residential Areas Number Authors, Years Country Context Research Methods Analysis Result 1 Akindele and Joseph, 2024 19 Nigeria Heavy metal content in indoor wall paint flakes in southwestern Nigerian homes. Experimental Inductively coupled plasma-optical emission spectrometer (ICP-OES) Indoor paint flakes in SW Nigeria contained high lead and cadmium, posing serious health risks, especially to children. 2 Allgood et al., 2024 20 United States Long-term health and social impacts of U.S. housing segregation on Black and White young adults. Longitudinal study General Estimating Equations (GEE) Black youths in mostly White neighborhoods had 8% higher CVD risk; White residents had 3% lower risk. 3 Chen et al., 2024 21 China Indoor heat risk during heatwaves in low-income housing in Guangzhou’s hot, humid climate. Simulation-based framework Statistical regression Heatwaves in Guangzhou increase illness risk in poorly insulated housing, especially with prolonged power outages. 4 Campos-Castillo et al., 2024 22 United States Mental health effects of residential hypersegregation on Latino adolescents in Milwaukee, USA. Semi-structured interviews Abductive analysis Social media helps youth cope with neighborhood stress but can also cause mental exhaustion with prolonged use. 5 Duan and Lin, 2024 23 China Health risks of PM2.5 exposure in children’s living environments in densely populated areas. Probabilistic Neural Networks (PNN) Probabilistic Neural Networks (PNN) Ultrafine particles significantly impact children's health; the model showed 84% accuracy in identifying health risks. 6 Hashemi et al., 2024 24 Republic of Korea Uranium exposure through private groundwater in areas with Jurassic granite aquifers in South Korea. Nationwide survey Monte-Carlo simulations Uranium in groundwater is higher in Jurassic granite areas, with exposure levels requiring strict risk management. 7 Kim et al., 2024 25 South Korea Emissions from various mosquito repellents and their impact on indoor air quality in homes. Experimental and panel study Monte Carlo simulation Coil- and mat-type repellents raise benzene levels; risks remain low, but chemical exposure warrants attention. 8 Liu et al., 2024 26 China Health risks of PM-bound toxic compounds from coal and biomass combustion in rural China. Experimental Emission factor calculation Clean coal reduces organic toxic risks but PTEs remain a concern; most harmful compounds found in fine particles. 9 Mngadi et al., 2024 27 South Africa Trace metal contamination in soil and vegetables near an abandoned gold mine in South Africa. Experimental Principal Component Analysis (PCA) Vegetables near a mine showed toxic metal accumulation, posing health risks (HRI > 1) despite containing nutrients. 10 Yang et al., 2024 28 China Cancer risk from shared cooking exhaust shaft systems in high-rise residential buildings in China. Multi-zone network simulation and experimental validation Health risk assessment using Inhalation Cancer Risk (ICR) based on TEQ for PAHs Poor exhaust systems in high-rise buildings raise cancer risks; larger shafts and sealed dampers reduce exposure. 11 Alani et al., 2023 29 Nigeria Environmental risks from flooding and poor drainage in Lagos’s flood-prone residential zones. Environmental risk assessment Pearson correlation Flood-prone areas showed low contamination but had cadmium-enriched soil and pesticide residues in groundwater. 12 Li et al., 2023 30 China CP emissions and distribution during residential interior finishing in China. Experimental Linear regression analysis Indoor finishing materials emitted high CP levels; floor wax was a major source, but health risks remained low. 13 Mbazima, 2023 31 South Africa Exposure to PM2.5-bound metal(loid)s near a ferromanganese smelter in Meyerton, South Africa. Health Risk Assessment Health Risk Assessment (HRA) Residents near smelters had high non-carcinogenic and carcinogenic risks from PM2.5-bound metals, especially Cr(VI). 14 Lu et al., 2023 32 China Vertical distribution and health risks of SCCPs and MCCPs in indoor dust in a multistory Beijing apartment. Experimental Principal Component Analysis (PCA) SCCPs and MCCPs decreased with floor height; current levels in indoor dust posed no significant health risks. 15 Okosa et al., 2023 33 Nigeria Heavy metal exposure from residential waste sites in Umuahia, Nigeria. Case study Contamination factor assessment Rural soils had higher heavy metal levels than urban; children face non-carcinogenic risks mainly via ingestion. 16 Sharma et al., 2023 34 India Indoor air pollution from biomass fuel use in rural kitchens of northeastern India. Field-based measurements Mass-size distribution analysis Biomass fuel users in rural kitchens had higher PM exposure and health risks, especially among children. 17 Goel et al., 2023 35 India Emissions and health risks of PAHs from common indoor air pollution sources in Indian homes. Experimental Gas Chromatography–Mass Spectrometry (GC-MS) Dhoops emitted the most PAHs; one type exceeded WHO limits, prompting need for regulation of indoor sources. 18 Shomar and Rashkeev, 2023 36 Qatar Health risks of trace metals in household dust from arid residential areas in Doha, Qatar. Experimental Inductively Coupled Plasma Mass Spectrometry (ICPMS) Dust in Doha posed no significant health risk via inhalation or skin contact across all seasons. 19 Wang et al., 2023 37 China PAH exposure risks in household dust from eight major Chinese cities under the National Health Surveillance. Observational study Principal Component Analysis (PCA) PAHs in household dust exceeded safe limits for children aged 0–5; key sources were combustion and traffic emissions 20 Xiao et al., 2022 38 China Chemical composition of atmospheric particulate matter in high lung cancer areas of rural Fuyuan, China. Environmental Health Risk Assessment Inductively Coupled Plasma Mass Spectrometry (ICP-MS) Fine particles in Fuyuan contained high selenium, contributing significantly to lung cancer risk. 21 Zhang et al., 2023 39 China Indoor PM2.5-bound PAHs in four major Chinese cities during winter and early spring. Case study Probabilistic risk assessment Indoor PAH levels varied by city; Xian had highest cancer risk from traffic-related outdoor infiltration. 22 Chiang et al., 2022 40 United States Impact of residential relocation on health and social risks among PWID in Los Angeles and San Francisco. Cross-sectional study Multivariable logistic regression Recent relocations among PWID linked to more violence, overdoses, food insecurity, and reduced access to services. 23 Ghassempour et al., 2022 41 Australia Impact of fire incidents from smoking materials before and after RFR cigarette regulation in Australia. Population-based cohort analysis Negative binomial regression analysis Post-regulation, fire incidents from cigarettes dropped by up to 22%, showing RFR effectiveness. 24 Li et al., 2022 42 China Indoor air pollution from cooking emissions in Chinese households. Experimental Chemical analysis Grilling caused the highest PM2.5 and PAHs levels; health risks from cooking emissions were above safe thresholds. 25 Liu et al., 2022 26 China Chemical composition and health risks of cooking fume condensates across Chinese regions. Experimental GC-MS analysis Cooking fumes contained 174 VOCs; regional differences were noted, with links to various chronic health risks. 26 Onwordi et al., 2022 43 Nigeria Groundwater quality and health risks in a residential estate in Lagos, Nigeria. Standard methods for water analysis Descriptive analysis and Pearson correlation coefficient Most groundwater was suitable for domestic use but required treatment; lead exceeded limits in all samples. 27 Peng et al., 2022 44 China Heavy metal accumulation in residential soils in Shanghai, Shenzhen, and Beijing, China. Field study and statistical analysis Linear regression analysis Heavy metal accumulation, especially Pb, was higher in older residential areas, increasing health risks. 28 Pachoulis et al., 2022 45 Greece Air pollution and health risks in an industrialized residential area in Greece. Health risk assessment Risk assessment using mathematical approaches Air pollution risks varied by method, but all remained within acceptable carcinogenic and non-carcinogenic limits. 29 Tang et al., 2022 46 China PFAS contamination and health risks in residential water bodies near fluorine chemical industrial parks in China. Environmental Science and Pollution Research Risk Quotient (RQ) methodology PFASs in water were higher in the wet season; infants faced the greatest health risks. 30 Algarni et al., 2021 47 Saudi Arabia Indoor air quality assessment in residential units in Abha, Saudi Arabia, during COVID-19 lockdown. Experimental Hazard quotient calculation Indoor PM and CO₂ levels exceeded safe limits during lockdown; aromatic smoke worsened indoor air quality. 31 Singh et al., 2021 48 India PAH concentrations in urban air across five residential sites in Delhi, India. Environmental monitoring Principal Component Analysis (PCA) PAHs in Delhi’s residential air came mainly from vehicles and wood; health risks remained within safe limits. 32 Tabatabaei et al., 2021 49 Iran Indoor air quality in homes with hookah use in Khesht, Iran. Cross-sectional study Monte Carlo simulation Children in homes with hookah smokers faced benzene exposure above safety limits, raising cancer risk. 33 Tsay et al., 2021 50 Taiwan Health impacts of indoor temperature and PM2.5 on elderly residents in Taiwanese buildings under climate change. Simulation Simulation Improving building design could cut health risk days by 48.5%; passive strategies alone may be insufficient. 34 Ali et al., 2020 51 Saudi Arabia Health risks from indoor dust exposure to As and Pb in Saudi homes of varying socio-economic status. Experimental Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) Urban dust had higher toxic metals than rural; risks to young children from dust exposure were significant. 35 Wickliffe et al., 2020 52 United States Assessment of cancer and non-cancer risks from indoor VOC exposure in low-income homes in southeast Louisiana. Cross-sectional air sampling GC-MS, probabilistic risk assessment Lifetime cancer risks from VOCs such as benzene, chloroform, and carbon tetrachloride exceeded acceptable thresholds in 35–50% of simulations. 36 Cheng et al., 2019 53 China PAH emissions from residential coal combustion under varying coal types and conditions in China. Experimental Emission factor calculation Flaming coal combustion emitted more PAHs than smoldering; fine particles carried most toxic load. 37 Ma et al., 2020 54 China Metal(loid) exposure risks from soil and indoor dust for children in Lanzhou, China. Experimental Pearson correlation coefficient Indoor dust had more bioaccessible metals than soil; arsenic posed the highest health risk to children. 38 Shabanda et al., 2019 55 Malaysia Heavy metal contamination in urban dust in Petaling Jaya, Malaysia. Environmental Science Monte Carlo simulation Urban dust showed high metal concentrations due to human activity; ingestion was main exposure route for children. 39 Siriwat et al., 2019 56 Thailand Cypermethrin exposure in agricultural households in northeastern Thailand. Cross-sectional study Spearman’s correlation Cypermethrin was present on multiple surfaces; overall risk was low, and hygiene practices reduced exposure. 40 Zhu et al., 2019 57 Taiwan PAH exposure from PM2.5 and PM10 in residential areas near industrial sites in Taichung, Taiwan. Environmental monitoring Principal Component Analysis (PCA) PAHs peaked in winter; vehicles were main source; children had the highest exposure-related health risks. 41 Li, 2018 58 Global Global assessment of POP pesticide exposure risks in residential soil across jurisdictions. Health risk characterization using disability-adjusted life years (DALYs) Cumulative distribution function analysis Some global pesticide soil RGVs showed high health risk variability; lindane had the lowest toxicity index. 42 McEwen et al., 2016 59 Bolivia Toxic metal exposure from adobe bricks and dirt floors in homes in Potosí, Bolivia. Experimental Inductively coupled plasma mass spectrometry (ICP-MS) Adobe homes in Potosí had high metal levels; children risked exposure to lead and arsenic via ingestion. 43 Ransohoff et al., 2016 60 United States Association between residential UV exposure in childhood vs. adulthood and risk of skin cancer in postmenopausal Caucasian women. Prospective cohort study Logistic regression, Cox proportional hazards model Adulthood UV exposure was associated with increased NMSC risk; no significant effect was found for childhood exposure or melanoma. 44 Kumar et al., 2015 61 India PAH-related health risks in urban residential soils of Gwalior, India. Environmental Risk Assessment Toxicity Equivalency and Probabilistic Health Risk Assessment PAH levels in Gwalior soils were within adult safety limits, but children may face elevated cancer risk. Table 1 summarizes 44 Scopus-indexed international studies addressing environmental health risks in residential areas, encompassing developed and developing countries. The topics investigated include heavy metal exposure, indoor air pollution, risks associated with building design and extreme temperatures, and socio-behavioral impacts such as segregation and relocation. The studies employ a wide range of research methods, including experimental designs (18 studies), observational and monitoring studies (9), cross-sectional studies (7), and simulation or mathematical modeling (5), with analytical techniques such as regression analysis, Monte Carlo simulations, and Principal Component Analysis (PCA). This table highlights the diversity of scientific approaches in assessing environmental impacts on public health within residential settings. Table 2 . Thematic Breakdown of Residential Environmental Health Risk Studies (n = 44) by Region, Design, Exposure Type, Population, and Variables Table 2 Thematic Breakdown of Residential Environmental Health Risk Studies (n = 44) by Region, Design, Exposure Type, Population, and Variables Classification Detailed Classification Count % of Total Geographic Distribution of Studies Asia 30 68.20% Africa 7 15.90% North America 3 6.80% Europe 2 4.50% Latin America & the Caribbean 1 2.30% Oceania 1 2.30% Total 44 100.00% Study Design Experimental / field & lab measurements 18 40.91% Observational/monitoring & case study 9 20.45% Cross-sectional 7 15.91% Simulation/Modeling 5 11.36% Cohort/Longitudinal 2 4.55% HRA/Secondary (predominant risk assessment) 3 6.82% Total 44 100.00% Type of Pollution Indoor Air Pollution 25 56.80% Soil and Dust Contamination 10 22.70% Water Contamination 3 6.80% Chemical Exposure (e.g. VOCs, Pesticides) 4 9.10% Heat and Climate Stress 2 4.50% Total 44 100.00% Affected Population Urban Residents 28 63.60% Children 10 22.70% Elderly 3 6.80% Low-Income Families 3 6.80% Total 44 100.00% Main Study Variables Heavy Metal Concentration 11 25.0% Air Quality 10 22.7% Carcinogenic Risk 8 18.2% Health Risk Assessment (General) 7 15.9% Use of Repellents or Indoor Chemicals 5 11.4% Other (UV, mental health, relocation) 3 6.8% Total 44 100.00% Table 2 shows clear skews across domains. Geographically, the evidence base is dominated by Asia (30 studies; 68.18%), while Latin America & the Caribbean and Oceania are each minimally represented (1 study; 2.27%). Methodologically, experimental designs—encompassing field and laboratory measurements—are most common (18; 40.91%), whereas cohort/longitudinal studies are the least used (2; 4.55%). By exposure type, indoor air pollution constitutes the principal focus (25; 56.82%), with heat and climate stress receiving the least attention (2; 4.55%). In terms of populations, urban residents are most frequently examined (28; 63.64%), while elderly groups and low-income families are the least represented (each 3; 6.82%). Finally, for study variables, heavy metal concentration is the most frequently assessed endpoint (11; 25.00%), whereas the “other” cluster (UV exposure, mental health, and relocation) appears least often (3; 6.82%). Collectively, these patterns highlight a regional concentration of evidence, a reliance on experimental measurement studies, a dominant focus on indoor air exposures, and notable underrepresentation of cohort designs and vulnerable subpopulations. As summarized in Table 2 and visualized in Fig. 2 , heavy metal concentration (n = 11) and air quality (n = 10) dominate the variable landscape, while carcinogenic risk (n = 8), general health risk assessment (n = 7), repellents/indoor chemicals (n = 5), and an “Other” cluster comprising UV exposure, mental-health outcomes, and residential relocation (n = 3) complete the distribution (total = 44). Figure 2 . Distribution of Key Variables in Residential Environmental Health Risk Studies Figure 2 visualizes the distribution of key study variables across the 44 articles. The most frequently analyzed variables were heavy metal concentration (n = 11; 25.0%) and air quality (n = 10; 22.7%), followed by carcinogenic risk (n = 8; 18.2%), general health risk assessment (n = 7; 15.9%), and use of repellents or indoor chemicals (n = 5; 11.4%). Notably, an "Other" cluster—encompassing UV exposure, mental health outcomes, and residential relocation—was identified in three studies (6.8%). These counts sum to 44 and align with Table 2 after reclassification, resolving the earlier inconsistency with Table 1 . Discussion In line with the results, residential environmental health risk is dominated by indoor air pollution (25/44; 56.8%), followed by soil/dust contamination (10/44; 22.7%), household chemicals (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%); this pattern aligns with the dominance of “heavy metal concentration” (25.0%) and “air quality” (22.7%) in Table 2 and Fig. 2 . 1) Residential Dominance of Indoor Air Pollution in Residential Health Risks The systematic review of 44 articles revealed that indoor air pollution is the most prevalent issue in the context of residential environmental health. Specifically, 25 of the 44 studies (56.8%) identified indoor air pollution as a significant concern, primarily associated with the use of solid fuels, inadequate ventilation, and exposure to household chemicals. Vulnerable groups, including children (10 studies), the elderly (3 studies), and low-income families (3 studies), were found to be the most adversely affected, particularly in densely populated urban areas where access to adequate environmental mitigation measures is limited. Indoor air pollution in residential settings profoundly impacts public health, especially among vulnerable populations. The review highlights that poor indoor air quality—primarily due to solid fuel use and inadequate ventilation—is a critical health issue affecting numerous households. Children are especially at risk due to their increased susceptibility to respiratory illnesses associated with indoor pollutants 62 , 63 . Urban environments with high population density often lack the proper infrastructure to manage this hazard, making low-income families disproportionately vulnerable 64 , 65 . Accordingly, clean-fuel transition and minimum ventilation upgrades emerge as the highest-leverage domestic interventions. Household practices such as using biomass fuels and cleaning chemicals contribute to increased concentrations of hazardous particles in indoor air 66 , 67 . This issue is further aggravated by the prolonged time that elderly individuals spend indoors, increasing their exposure risk 68 . As a result, targeted and effective interventions are urgently needed to reduce indoor air pollution and protect the health of high-risk populations 69 , 70 . The involvement of vulnerable groups remains limited, with children (22.7%), older adults (6.8%), and low-income families (6.8%) being the primary focus, underscoring the urgency of equity-oriented interventions in dense urban settings. 2) Sources of Domestic Exposure and Chronic Health Effects The findings suggest that domestic activities contribute to indoor pollutant exposure, with associated health risks ranging from respiratory infections to cardiovascular diseases and cancer. These observations align with research by Sharpe et al., which linked inadequate airflow to increased concentrations of microorganisms and harmful particulates inside homes 71 . Household practices significantly contribute to indoor pollution exposure, triggering a range of adverse health outcomes, including respiratory disorders, cardiac issues, and oncological risks. Chavan et al. emphasized the global burden of solid fuel use in developing countries, which is closely associated with high mortality from indoor air pollution. According to the World Health Organization, approximately 3.8 million deaths yearly are attributable to household air pollution, with solid fuel smoke among the top ten global risk factors 72 . Evidence across studies indicates co-exposure to PM2.5, VOCs, and PAHs indoors; risk appraisal should therefore adopt a multi-pollutant lens rather than a single-pollutant approach. Poor ventilation substantially worsens indoor air quality by enabling the accumulation of toxic particulates. Maung et al. 73 demonstrated a direct correlation between inadequate airflow and elevated particle concentrations, a finding echoed by Lee et al. 74 who observed that prolonged exposure to such pollutants heightens the risk of respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Furthermore, Li et al. 75 highlighted that indoor combustion significantly increases acceptable particulate matter levels, compromising overall air quality. Notably, Maung et al. also reported that participants spend most of their time indoors, particularly during sleep, reinforcing the urgency of indoor air pollution control measures. Empirical evidence consistently supports the strong association between exposure to fine particles and volatile organic compounds (VOCs) at high concentrations and increased risks of asthma and other respiratory ailments. Research has also shown that homes in poor condition often lack adequate ventilation, exacerbating pollutant exposure from activities like cooking 76 . Chakraborty et al. 77 emphasized that individuals from lower socioeconomic backgrounds tend to spend more time indoors, increasing their risk of pollutant exposure. Children from low-income households living in poorly ventilated homes are especially vulnerable to adverse health impacts. High-density housing and low-quality building materials also contribute to pollutant accumulation and pose health risks to residents 77 , 78 . Domestic pollution, poor ventilation, and social inequality heighten environmental health risks. Therefore, a comprehensive public health strategy is essential. Key actions include improving building design and ventilation systems to mitigate exposure and protect susceptible populations. 3) Hidden Risks from Soil, Dust, and Household Water Exposure to contaminants through soil, dust, and household water remains an often overlooked public health risk in residential environments. Several studies have revealed high levels of heavy metal contamination in domestic settings due to anthropogenic activities. For instance, Jabbarov et al. 79 reported the presence of lead (Pb), zinc (Zn), and cadmium (Cd) around domestic waste areas and vehicle emissions; however, their data are more applicable to waste disposal zones than general residential neighborhoods. Al-Swadi et al. 80 found that heavy metal contamination levels vary between urban and suburban areas, depending on proximity to industrial and mining activities. Additionally, dust pollution from industries such as cement production negatively affects soil microbial activity, disrupts biogeochemical cycles, and poses health risks. Specifically, untreated cement particles can alter the physicochemical properties of soil, reducing microbial populations and overall soil health 81 . This reduction in microbial diversity correlates with nutrient cycle disruption, vital for soil fertility and agricultural productivity 82 . Dust also impairs soil moisture retention, exacerbating its impact on microbial dynamics and environmental health 83 . Consequently, dust pollution is a significant environmental issue affecting terrestrial ecosystems and public health through multiple soil quality–related exposure pathways 81 , 83 . Contaminated drinking water due to pesticides and heavy metals is also a growing concern. El-Saeid et al. 84 , noted that polycyclic aromatic hydrocarbons (PAHs) from vehicle emissions can infiltrate soil and dust, posing health risks when inhaled. A study in Lebanon revealed contamination of water sources by various pesticides, with each concentration directly impacting drinking water quality 85 . Similarly, Bawa et al. estimated high health risks from consuming crops contaminated by heavy metals, underscoring the dangers of uncontrolled agricultural practices 86 . Furthermore, indiscriminate pesticide use has been linked to degraded water quality and hazardous ecological impacts due to pollutant accumulation in aquatic ecosystems 87 . As observed in the Pearl River Delta region, the infiltration of PAHs from vehicle emissions into soil further worsens environmental health conditions 88 . 4) Geographic Gaps and Methodological Challenges in the Literature The geographic distribution of studies highlights significant disparities, particularly regarding the Global South. Most studies originated from Asia (68%) and Africa (16%), revealing a stark geographic bias that hampers the generalization of global policy recommendations. This research concentration in relatively better-resourced regions aligns with findings in public administration literature, which show that wealthier areas receive greater academic attention while less affluent regions are marginalized 89 , 90 . This pattern perpetuates a cycle of exclusion in which the voices and experiences of underrepresented communities are frequently ignored in global discourse 91 , 92 . The evidence base is concentrated in Asia (68.2%) and Africa (15.9%), whereas Latin America & the Caribbean and Oceania each contribute only 2.3%; methodologically, experimental/field–lab studies dominate (40.9%) and cohorts remain rare (4.5%). This distribution limits global generalizability and long-term causal inference and underscores the need for longitudinal designs and participatory approaches. Such bias impairs our understanding of local mitigation behaviors and the unique experiences of affected communities. The lack of community-based participatory approaches exacerbates the issue, even though such methods are essential for capturing contextual realities 93 , 94 . While recent studies have underscored the need for diverse methodological frameworks—including qualitative studies—the literature remains dominated by quantitative experimental designs that fail to reflect community perspectives 90 , 95 . This omission overlooks critical local insights and risks deepening disparities in representation and research outcomes. The consequences of this bias extend to funding priorities and the agenda-setting for environmental and social issues. Research from regions with higher academic capacity and funding tends to be prioritized 89 . In contrast, Latin America and Oceania remain underrepresented despite their important ecological and social contexts for shaping global policy 96 . This imbalance threatens the validity and applicability of scientific findings at the international level. Expanding the geographic scope and adopting qualitative methodologies are vital to reducing bias, improving policy relevance, and fostering a more inclusive understanding of challenges and solutions across global communities. Conceptually, these findings identify intervention entry points across a One Health triad—encompassing people, the residential environment, and domestic practices—with a focus on clean-fuel transitions, minimum ventilation, and risk communication related to household chemicals. Harmonizing cross-media metrics (air–dust/soil–water) is essential so that policy performance indicators can be tracked across places and over time. Conclusion Across 44 studies, residential environmental health risk is dominated by indoor air pollution (25/44; 56.8%), followed by soil/dust contamination (10/44; 22.7%), household chemical exposure (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%). The evidence base is geographically concentrated in Asia (68.2%) and Africa (15.9%), with Latin America & the Caribbean and Oceania each contributing only 2.3%, limiting global generalizability. Methodologically, experimental/field–lab designs prevail (40.9%), while cohort/longitudinal studies are rare (4.5%). Urban residents are most frequently examined (63.6%), and vulnerable groups include children (22.7%), older adults (6.8%), and low-income families (6.8%). The most analyzed endpoints—heavy-metal concentrations (25.0%) and air quality (22.7%)—indicate a multi-pathway but indoor-air-centric burden, with geographic and methodological gaps constraining long-term causal inference and equity-minded policy translation. Recommendations Policy and practice should prioritize clean-fuel transitions, minimum ventilation/building standards, and targeted risk communication and management of household chemicals (clear labeling, safe storage, safer substitutes), with tailored emphasis on children, older adults, and low-income households in dense urban settings. Non-air pathways must be integrated via dust/soil management (scheduled wet cleaning, source control) and risk-based household water testing and treatment. To strengthen the evidence base, future work should expand to under-represented regions (Latin America & Oceania), adopt cohort/longitudinal and community-based participatory designs, and harmonize HRA/indicator metrics across air–dust/soil–water so findings are comparable across places and over time. All actions are best organized within a One Health framework to align health–environment–housing interventions from community practice to national policy. Declarations Trial registration number: Not applicable. Conflict of interest The authors declare that there is no competing interest. Funding The work was not funded. Author Contribution Kholis Ernawati conceived and designed the study, conducted the literature search and data extraction, performed the analysis and interpretation, and wrote and revised the manuscript. 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08:41:45","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208788,"visible":true,"origin":"","legend":"","description":"","filename":"7f76e2e2fe954bec9876c5f68d5572e41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7994301/v1/3d683725b9df1ece29064bc5.xml"},{"id":95278392,"identity":"60821eeb-17ac-454e-828a-7bda4fefcd86","added_by":"auto","created_at":"2025-11-06 08:41:45","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213613,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7994301/v1/61a928cccd6874aa492804b2.html"},{"id":95278380,"identity":"40d7fd70-570a-4143-bf9a-dd7f2ff3dc7f","added_by":"auto","created_at":"2025-11-06 08:41:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92841,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Diagram of the Article Selection Process.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7994301/v1/7ff829111ddd2fc61bd23f9b.png"},{"id":95278385,"identity":"dcc18a89-b42d-4bf1-82aa-5ea23b69c9bb","added_by":"auto","created_at":"2025-11-06 08:41:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325514,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Key Variables in Residential Environmental Health Risk Studies.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7994301/v1/06c0e13660d1578d594a3e8c.jpeg"},{"id":97140015,"identity":"99e737f2-a8bd-4079-829a-c102fba838c2","added_by":"auto","created_at":"2025-12-01 10:03:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7994301/v1/131e1ab4-1c07-436e-a873-e67bec0561cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Health Risks in Residential Settings: A Systematic Review of Indoor Air Pollution, Domestic Chemical Exposure, and Global Representation Inequities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe intersection of climate change, urbanization, and domestic chemical exposure underscores the urgency of understanding environmental risks within residential contexts. Empirical evidence shows that indoor air quality (IAQ) can be significantly worse than outdoor air, with pollutant concentrations inside homes reported to be two to five times higher than those outdoors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Key sources of indoor air pollution include emissions from cooking, volatile organic compounds (VOCs) from building materials, and pesticide residues. Climate change further exacerbates this issue by affecting outdoor air quality and intensifying urban heat island effects.\u003c/p\u003e\u003cp\u003eRapid urbanization has particularly adverse effects on low-income communities, which are more susceptible to indoor air pollution due to overcrowded living conditions, inadequate ventilation, and high-pollution household practices\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Additional exposure risks arise from proximity to traffic, industrial zones, and indoor heating and cooking systems\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHousehold chemicals, including VOCs from cleaning agents and paints, add to the complexity of indoor pollution. While many studies focus on individual pollutants, they overlook their cumulative effects. Thus, an integrated approach is required to assess the various sources of indoor contamination and their interactions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Lifestyle, behavior, and cultural norms significantly influence exposure levels, and interventions' effectiveness\u0026mdash;such as indoor plants or ventilation systems\u0026mdash;depend largely on regional practices and preferences\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Geographic and climatic differences also play a crucial role in determining the success of mitigation strategies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, geographic disparities in IAQ research reveal methodological bias. Most studies have focused on high-income countries, neglecting developing nations' distinct environmental challenges\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Green technologies effective in developed urban areas may not be feasible in regions with limited infrastructure\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, environmental events such as wildfires have worsened indoor air conditions by increasing the infiltration of smoke, particulate matter, and polycyclic aromatic hydrocarbons (PAHs) into residential spaces\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Climate-induced lifestyle stressors further intensify indoor pollution dynamics.\u003c/p\u003e\u003cp\u003eAddressing indoor air quality in residential environments requires a multidimensional strategy that includes technological innovation, education, and regulatory reform. Since individuals spend most of their time indoors, public policies should prioritize improving indoor environmental quality, especially for vulnerable populations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Technological innovations such as bioremediation and advanced filtration systems must be tailored to local cultural and socioeconomic contexts. The interplay between climate, urbanization, and household chemicals necessitates a comprehensive framework for improving the quality of residential air environments. Collaborative efforts are needed to tackle socioeconomic disparities and foster healthier living conditions.\u003c/p\u003e\u003cp\u003eThis study aims to (1) Identify dominant trends and thematic patterns in the literature concerning environmental health risks in residential settings; (2) Classify types of pollution, research methods, affected populations, and frequently studied variables; (3) Analyze methodological and geographic gaps that hinder the development of globally relevant policies and (4) Offer policy and research recommendations through a cross-sectoral One Health integration approach.\u003c/p\u003e\u003cp\u003eThis study offers novelty on two levels: content and approach. In terms of content, we not only map domestic environmental exposures (indoor air, dust/soil, water, and household chemicals), but also quantify the geographical representation gap in global research that has implications for policy equity. In terms of approach, we developed an antecedent\u0026ndash;mediator\u0026ndash;consequence network that integrates various key variables and guides the determination of intervention entry points. The combination of both produces One Health recommendations based on patterns of findings\u0026mdash;prioritizing clean fuel transition, ventilation improvements, and chemical education, while also encouraging the expansion of study coverage and participatory study design to close the evidence gap across regions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study is a Systematic Literature Review (SLR) conducted to identify, classify, and synthesize scientific evidence related to health risks resulting from environmental exposure in residential areas. The review was structured by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e1. Study Design and Theoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis SLR is not purely descriptive; it also explores the relationships among variables within a framework of antecedents (causes), mediators (intervening factors), and consequences (outcomes) of environmental health risks. To support this, a concept matrix and hypothesis network analysis were employed to map findings and identify significant research gaps.\u003c/p\u003e\u003cp\u003eThe analytical framework refers to Miles' taxonomy of research gaps\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, developed from the model by Robinson et al.\u003csup\u003e16\u003c/sup\u003e and further refined by M\u0026uuml;ller-Bloch and Kranz\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Literature synthesis followed Cooper's taxonomy of literature reviews\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which emphasizes a neutral approach and organizes information within a historical and conceptual context without directly critiquing content.\u003c/p\u003e\n\u003ch3\u003e2. Data Sources and Search Strategy\u003c/h3\u003e\n\u003cp\u003eScientific literature was gathered from the Scopus database and selected for its comprehensive coverage of reputable academic publications. The keywords used in the search process included: \u003cem\u003e\"residential health risk,\" \"environmental health risk,\" \"household,\"\u003c/em\u003e and \u003cem\u003e\"indoor pollution,\"\u003c/em\u003e combined using logical operators (AND/OR). The search was limited to journal articles published between January 2015 and March 2025, written in English, and available in full-text format.\u003c/p\u003e\n\u003ch3\u003e3. Article Selection and Exclusion\u003c/h3\u003e\n\u003cp\u003eThe article selection process followed the four standard PRISMA phases: identification, screening, eligibility assessment, and inclusion. Of the initial 162 articles identified, 53 were excluded for not meeting technical or substantive criteria. One hundred nine articles were screened based on titles and abstracts, but 61 were inaccessible in full text. Finally, 48 articles underwent a complete eligibility assessment, and 44 were included in the final review. The entire selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (PRISMA Diagram).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo ensure the validity and reproducibility of the selection process, the PRISMA diagram was created using Watase Uake Tools, a PRISMA 2020\u0026ndash;compliant reporting software that facilitates a systematic, concise, and auditable visualization of the selection flow.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. PRISMA Diagram of the Article Selection Process\u003c/p\u003e\n\u003ch3\u003e4. Data Extraction and Synthesis\u003c/h3\u003e\n\u003cp\u003eThe selected articles were analyzed using a thematic matrix that included the components of pollution, pollution-generating activities, affected population groups, primary study variables, methodological design, study location, and journal quartile rankings (Scimago Q1\u0026ndash;Q4).\u003c/p\u003e\u003cp\u003eThe synthesis process was conducted descriptively and conceptually to identify patterns in study distribution, emerging thematic trends, and knowledge gaps, which may serve as a foundation for shaping future research directions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe following section presents a compilation of 44 international studies investigating environmental health risks in residential settings. These studies span diverse geographic regions and research methodologies, providing insights into pollution exposure pathways, vulnerable populations, and analytical approaches.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. International Research Database on Environmental Health Risks in Residential Areas\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInternational Research Database on Environmental Health Risks in Residential Areas\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAuthors, Years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContext\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResearch Methods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkindele and Joseph,\u0026nbsp;2024 \u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNigeria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy metal content in indoor wall paint flakes in southwestern Nigerian homes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInductively coupled plasma-optical emission spectrometer (ICP-OES)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndoor paint flakes in SW Nigeria contained high lead and cadmium, posing serious health risks, especially to children.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllgood et al.,\u0026nbsp;2024 \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLong-term health and social impacts of U.S. housing segregation on Black and White young adults.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLongitudinal study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGeneral Estimating Equations (GEE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBlack youths in mostly White neighborhoods had 8% higher CVD risk; White residents had 3% lower risk.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChen et al.,\u0026nbsp;2024 \u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor heat risk during heatwaves in low-income housing in Guangzhou\u0026rsquo;s hot, humid climate.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimulation-based framework\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStatistical regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHeatwaves in Guangzhou increase illness risk in poorly insulated housing, especially with prolonged power outages.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCampos-Castillo et al.,\u0026nbsp;2024 \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMental health effects of residential hypersegregation on Latino adolescents in Milwaukee, USA.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSemi-structured interviews\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbductive analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSocial media helps youth cope with neighborhood stress but can also cause mental exhaustion with prolonged use.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuan and Lin,\u0026nbsp;2024 \u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth risks of PM2.5 exposure in children\u0026rsquo;s living environments in densely populated areas.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbabilistic Neural Networks (PNN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProbabilistic Neural Networks (PNN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUltrafine particles significantly impact children's health; the model showed 84% accuracy in identifying health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHashemi et al.,\u0026nbsp;2024 \u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRepublic of Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUranium exposure through private groundwater in areas with Jurassic granite aquifers in South Korea.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNationwide survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMonte-Carlo simulations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUranium in groundwater is higher in Jurassic granite areas, with exposure levels requiring strict risk management.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKim et al.,\u0026nbsp;2024 \u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSouth Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEmissions from various mosquito repellents and their impact on indoor air quality in homes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental and panel study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMonte Carlo simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoil- and mat-type repellents raise benzene levels; risks remain low, but chemical exposure warrants attention.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiu et al.,\u0026nbsp;2024 \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth risks of PM-bound toxic compounds from coal and biomass combustion in rural China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEmission factor calculation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eClean coal reduces organic toxic risks but PTEs remain a concern; most harmful compounds found in fine particles.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMngadi et al.,\u0026nbsp;2024 \u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSouth Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrace metal contamination in soil and vegetables near an abandoned gold mine in South Africa.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVegetables near a mine showed toxic metal accumulation, posing health risks (HRI\u0026thinsp;\u0026gt;\u0026thinsp;1) despite containing nutrients.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYang et al.,\u0026nbsp;2024 \u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCancer risk from shared cooking exhaust shaft systems in high-rise residential buildings in China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMulti-zone network simulation and experimental validation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHealth risk assessment using Inhalation Cancer Risk (ICR) based on TEQ for PAHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePoor exhaust systems in high-rise buildings raise cancer risks; larger shafts and sealed dampers reduce exposure.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlani et al.,\u0026nbsp;2023 \u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNigeria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironmental risks from flooding and poor drainage in Lagos\u0026rsquo;s flood-prone residential zones.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePearson correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFlood-prone areas showed low contamination but had cadmium-enriched soil and pesticide residues in groundwater.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLi et al.,\u0026nbsp;2023 \u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCP emissions and distribution during residential interior finishing in China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLinear regression analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndoor finishing materials emitted high CP levels; floor wax was a major source, but health risks remained low.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMbazima,\u0026nbsp;2023 \u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSouth Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExposure to PM2.5-bound metal(loid)s near a ferromanganese smelter in Meyerton, South Africa.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth Risk Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHealth Risk Assessment (HRA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eResidents near smelters had high non-carcinogenic and carcinogenic risks from PM2.5-bound metals, especially Cr(VI).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLu et al.,\u0026nbsp;2023 \u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVertical distribution and health risks of SCCPs and MCCPs in indoor dust in a multistory Beijing apartment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSCCPs and MCCPs decreased with floor height; current levels in indoor dust posed no significant health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOkosa et al.,\u0026nbsp;2023 \u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNigeria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy metal exposure from residential waste sites in Umuahia, Nigeria.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCase study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eContamination factor assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRural soils had higher heavy metal levels than urban; children face non-carcinogenic risks mainly via ingestion.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSharma et al.,\u0026nbsp;2023 \u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor air pollution from biomass fuel use in rural kitchens of northeastern India.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eField-based measurements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMass-size distribution analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBiomass fuel users in rural kitchens had higher PM exposure and health risks, especially among children.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoel et al.,\u0026nbsp;2023 \u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEmissions and health risks of PAHs from common indoor air pollution sources in Indian homes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGas Chromatography\u0026ndash;Mass Spectrometry (GC-MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDhoops emitted the most PAHs; one type exceeded WHO limits, prompting need for regulation of indoor sources.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShomar and Rashkeev,\u0026nbsp;2023 \u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQatar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth risks of trace metals in household dust from arid residential areas in Doha, Qatar.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInductively Coupled Plasma Mass Spectrometry (ICPMS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDust in Doha posed no significant health risk via inhalation or skin contact across all seasons.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWang et al.,\u0026nbsp;2023 \u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAH exposure risks in household dust from eight major Chinese cities under the National Health Surveillance.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObservational study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePAHs in household dust exceeded safe limits for children aged 0\u0026ndash;5; key sources were combustion and traffic emissions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXiao et al.,\u0026nbsp;2022 \u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChemical composition of atmospheric particulate matter in high lung cancer areas of rural Fuyuan, China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental Health Risk Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInductively Coupled Plasma Mass Spectrometry (ICP-MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFine particles in Fuyuan contained high selenium, contributing significantly to lung cancer risk.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZhang et al.,\u0026nbsp;2023 \u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor PM2.5-bound PAHs in four major Chinese cities during winter and early spring.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCase study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProbabilistic risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndoor PAH levels varied by city; Xian had highest cancer risk from traffic-related outdoor infiltration.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChiang et al.,\u0026nbsp;2022 \u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImpact of residential relocation on health and social risks among PWID in Los Angeles and San Francisco.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCross-sectional study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultivariable logistic regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecent relocations among PWID linked to more violence, overdoses, food insecurity, and reduced access to services.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhassempour et al.,\u0026nbsp;2022 \u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImpact of fire incidents from smoking materials before and after RFR cigarette regulation in Australia.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePopulation-based cohort analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNegative binomial regression analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePost-regulation, fire incidents from cigarettes dropped by up to 22%, showing RFR effectiveness.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLi et al.,\u0026nbsp;2022 \u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor air pollution from cooking emissions in Chinese households.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChemical analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGrilling caused the highest PM2.5 and PAHs levels; health risks from cooking emissions were above safe thresholds.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiu et al.,\u0026nbsp;2022 \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChemical composition and health risks of cooking fume condensates across Chinese regions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGC-MS analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCooking fumes contained 174 VOCs; regional differences were noted, with links to various chronic health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnwordi et al.,\u0026nbsp;2022 \u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNigeria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroundwater quality and health risks in a residential estate in Lagos, Nigeria.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard methods for water analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDescriptive analysis and Pearson correlation coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMost groundwater was suitable for domestic use but required treatment; lead exceeded limits in all samples.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeng et al.,\u0026nbsp;2022 \u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy metal accumulation in residential soils in Shanghai, Shenzhen, and Beijing, China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eField study and statistical analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLinear regression analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHeavy metal accumulation, especially Pb, was higher in older residential areas, increasing health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePachoulis et al.,\u0026nbsp;2022 \u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreece\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAir pollution and health risks in an industrialized residential area in Greece.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRisk assessment using mathematical approaches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAir pollution risks varied by method, but all remained within acceptable carcinogenic and non-carcinogenic limits.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTang et al.,\u0026nbsp;2022 \u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePFAS contamination and health risks in residential water bodies near fluorine chemical industrial parks in China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental Science and Pollution Research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRisk Quotient (RQ) methodology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePFASs in water were higher in the wet season; infants faced the greatest health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlgarni et al.,\u0026nbsp;2021 \u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor air quality assessment in residential units in Abha, Saudi Arabia, during COVID-19 lockdown.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHazard quotient calculation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndoor PM and CO₂ levels exceeded safe limits during lockdown; aromatic smoke worsened indoor air quality.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingh et al.,\u0026nbsp;2021 \u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAH concentrations in urban air across five residential sites in Delhi, India.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePAHs in Delhi\u0026rsquo;s residential air came mainly from vehicles and wood; health risks remained within safe limits.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTabatabaei et al.,\u0026nbsp;2021 \u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndoor air quality in homes with hookah use in Khesht, Iran.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCross-sectional study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMonte Carlo simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChildren in homes with hookah smokers faced benzene exposure above safety limits, raising cancer risk.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTsay et al.,\u0026nbsp;2021 \u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth impacts of indoor temperature and PM2.5 on elderly residents in Taiwanese buildings under climate change.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSimulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eImproving building design could cut health risk days by 48.5%; passive strategies alone may be insufficient.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAli et al.,\u0026nbsp;2020 \u003csup\u003e51\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth risks from indoor dust exposure to As and Pb in Saudi homes of varying socio-economic status.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInductively Coupled Plasma Mass Spectroscopy (ICP-MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUrban dust had higher toxic metals than rural; risks to young children from dust exposure were significant.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWickliffe et al., 2020 \u003csup\u003e52\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssessment of cancer and non-cancer risks from indoor VOC exposure in low-income homes in southeast Louisiana.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCross-sectional air sampling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGC-MS, probabilistic risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLifetime cancer risks from VOCs such as benzene, chloroform, and carbon tetrachloride exceeded acceptable thresholds in 35\u0026acirc;\u0026euro;\u0026ldquo;50% of simulations.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCheng et al.,\u0026nbsp;2019 \u003csup\u003e53\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAH emissions from residential coal combustion under varying coal types and conditions in China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEmission factor calculation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFlaming coal combustion emitted more PAHs than smoldering; fine particles carried most toxic load.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMa et al.,\u0026nbsp;2020 \u003csup\u003e54\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetal(loid) exposure risks from soil and indoor dust for children in Lanzhou, China.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePearson correlation coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndoor dust had more bioaccessible metals than soil; arsenic posed the highest health risk to children.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShabanda et al.,\u0026nbsp;2019 \u003csup\u003e55\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy metal contamination in urban dust in Petaling Jaya, Malaysia.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMonte Carlo simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUrban dust showed high metal concentrations due to human activity; ingestion was main exposure route for children.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSiriwat et al.,\u0026nbsp;2019 \u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThailand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCypermethrin exposure in agricultural households in northeastern Thailand.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCross-sectional study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpearman\u0026rsquo;s correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCypermethrin was present on multiple surfaces; overall risk was low, and hygiene practices reduced exposure.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZhu et al.,\u0026nbsp;2019 \u003csup\u003e57\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAH exposure from PM2.5 and PM10 in residential areas near industrial sites in Taichung, Taiwan.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePAHs peaked in winter; vehicles were main source; children had the highest exposure-related health risks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLi,\u0026nbsp;2018 \u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGlobal assessment of POP pesticide exposure risks in residential soil across jurisdictions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth risk characterization using disability-adjusted life years (DALYs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative distribution function analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSome global pesticide soil RGVs showed high health risk variability; lindane had the lowest toxicity index.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMcEwen et al.,\u0026nbsp;2016 \u003csup\u003e59\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBolivia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eToxic metal exposure from adobe bricks and dirt floors in homes in Potos\u0026iacute;, Bolivia.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInductively coupled plasma mass spectrometry (ICP-MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdobe homes in Potos\u0026iacute; had high metal levels; children risked exposure to lead and arsenic via ingestion.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRansohoff et al., 2016 \u003csup\u003e60\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssociation between residential UV exposure in childhood vs. adulthood and risk of skin cancer in postmenopausal Caucasian women.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProspective cohort study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLogistic regression, Cox proportional hazards model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdulthood UV exposure was associated with increased NMSC risk; no significant effect was found for childhood exposure or melanoma.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKumar et al.,\u0026nbsp;2015 \u003csup\u003e61\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAH-related health risks in urban residential soils of Gwalior, India.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental Risk Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eToxicity Equivalency and Probabilistic Health Risk Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePAH levels in Gwalior soils were within adult safety limits, but children may face elevated cancer risk.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes 44 Scopus-indexed international studies addressing environmental health risks in residential areas, encompassing developed and developing countries. The topics investigated include heavy metal exposure, indoor air pollution, risks associated with building design and extreme temperatures, and socio-behavioral impacts such as segregation and relocation. The studies employ a wide range of research methods, including experimental designs (18 studies), observational and monitoring studies (9), cross-sectional studies (7), and simulation or mathematical modeling (5), with analytical techniques such as regression analysis, Monte Carlo simulations, and Principal Component Analysis (PCA). This table highlights the diversity of scientific approaches in assessing environmental impacts on public health within residential settings.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Thematic Breakdown of Residential Environmental Health Risk Studies (n\u0026thinsp;=\u0026thinsp;44) by Region, Design, Exposure Type, Population, and Variables\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThematic Breakdown of Residential Environmental Health Risk Studies (n\u0026thinsp;=\u0026thinsp;44) by Region, Design, Exposure Type, Population, and Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetailed Classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of Total\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eGeographic Distribution of Studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAfrica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorth America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEurope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLatin America \u0026amp; the Caribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eStudy Design\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExperimental / field \u0026amp; lab measurements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservational/monitoring \u0026amp; case study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.45%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCross-sectional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimulation/Modeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.36%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort/Longitudinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.55%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHRA/Secondary (predominant risk assessment)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eType of Pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndoor Air Pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil and Dust Contamination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater Contamination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemical Exposure (e.g. VOCs, Pesticides)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeat and Climate Stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAffected Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElderly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-Income Families\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eMain Study Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy Metal Concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAir Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarcinogenic Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth Risk Assessment (General)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUse of Repellents or Indoor Chemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther (UV, mental health, relocation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows clear skews across domains. Geographically, the evidence base is dominated by Asia (30 studies; 68.18%), while Latin America \u0026amp; the Caribbean and Oceania are each minimally represented (1 study; 2.27%). Methodologically, experimental designs\u0026mdash;encompassing field and laboratory measurements\u0026mdash;are most common (18; 40.91%), whereas cohort/longitudinal studies are the least used (2; 4.55%). By exposure type, indoor air pollution constitutes the principal focus (25; 56.82%), with heat and climate stress receiving the least attention (2; 4.55%). In terms of populations, urban residents are most frequently examined (28; 63.64%), while elderly groups and low-income families are the least represented (each 3; 6.82%). Finally, for study variables, heavy metal concentration is the most frequently assessed endpoint (11; 25.00%), whereas the \u0026ldquo;other\u0026rdquo; cluster (UV exposure, mental health, and relocation) appears least often (3; 6.82%). Collectively, these patterns highlight a regional concentration of evidence, a reliance on experimental measurement studies, a dominant focus on indoor air exposures, and notable underrepresentation of cohort designs and vulnerable subpopulations.\u003c/p\u003e\u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, heavy metal concentration (n\u0026thinsp;=\u0026thinsp;11) and air quality (n\u0026thinsp;=\u0026thinsp;10) dominate the variable landscape, while carcinogenic risk (n\u0026thinsp;=\u0026thinsp;8), general health risk assessment (n\u0026thinsp;=\u0026thinsp;7), repellents/indoor chemicals (n\u0026thinsp;=\u0026thinsp;5), and an \u0026ldquo;Other\u0026rdquo; cluster comprising UV exposure, mental-health outcomes, and residential relocation (n\u0026thinsp;=\u0026thinsp;3) complete the distribution (total\u0026thinsp;=\u0026thinsp;44).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Distribution of Key Variables in Residential Environmental Health Risk Studies\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the distribution of key study variables across the 44 articles. The most frequently analyzed variables were heavy metal concentration (n\u0026thinsp;=\u0026thinsp;11; 25.0%) and air quality (n\u0026thinsp;=\u0026thinsp;10; 22.7%), followed by carcinogenic risk (n\u0026thinsp;=\u0026thinsp;8; 18.2%), general health risk assessment (n\u0026thinsp;=\u0026thinsp;7; 15.9%), and use of repellents or indoor chemicals (n\u0026thinsp;=\u0026thinsp;5; 11.4%). Notably, an \"Other\" cluster\u0026mdash;encompassing UV exposure, mental health outcomes, and residential relocation\u0026mdash;was identified in three studies (6.8%). These counts sum to 44 and align with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e after reclassification, resolving the earlier inconsistency with Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn line with the results, residential environmental health risk is dominated by indoor air pollution (25/44; 56.8%), followed by soil/dust contamination (10/44; 22.7%), household chemicals (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%); this pattern aligns with the dominance of \u0026ldquo;heavy metal concentration\u0026rdquo; (25.0%) and \u0026ldquo;air quality\u0026rdquo; (22.7%) in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003e1) Residential Dominance of Indoor Air Pollution in Residential Health Risks\u003c/h3\u003e\n\u003cp\u003eThe systematic review of 44 articles revealed that indoor air pollution is the most prevalent issue in the context of residential environmental health. Specifically, 25 of the 44 studies (56.8%) identified indoor air pollution as a significant concern, primarily associated with the use of solid fuels, inadequate ventilation, and exposure to household chemicals. Vulnerable groups, including children (10 studies), the elderly (3 studies), and low-income families (3 studies), were found to be the most adversely affected, particularly in densely populated urban areas where access to adequate environmental mitigation measures is limited.\u003c/p\u003e\u003cp\u003eIndoor air pollution in residential settings profoundly impacts public health, especially among vulnerable populations. The review highlights that poor indoor air quality\u0026mdash;primarily due to solid fuel use and inadequate ventilation\u0026mdash;is a critical health issue affecting numerous households. Children are especially at risk due to their increased susceptibility to respiratory illnesses associated with indoor pollutants \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Urban environments with high population density often lack the proper infrastructure to manage this hazard, making low-income families disproportionately vulnerable\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Accordingly, clean-fuel transition and minimum ventilation upgrades emerge as the highest-leverage domestic interventions.\u003c/p\u003e\u003cp\u003eHousehold practices such as using biomass fuels and cleaning chemicals contribute to increased concentrations of hazardous particles in indoor air\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. This issue is further aggravated by the prolonged time that elderly individuals spend indoors, increasing their exposure risk\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. As a result, targeted and effective interventions are urgently needed to reduce indoor air pollution and protect the health of high-risk populations\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The involvement of vulnerable groups remains limited, with children (22.7%), older adults (6.8%), and low-income families (6.8%) being the primary focus, underscoring the urgency of equity-oriented interventions in dense urban settings.\u003c/p\u003e\n\u003ch3\u003e2) Sources of Domestic Exposure and Chronic Health Effects\u003c/h3\u003e\n\u003cp\u003eThe findings suggest that domestic activities contribute to indoor pollutant exposure, with associated health risks ranging from respiratory infections to cardiovascular diseases and cancer. These observations align with research by Sharpe et al., which linked inadequate airflow to increased concentrations of microorganisms and harmful particulates inside homes\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Household practices significantly contribute to indoor pollution exposure, triggering a range of adverse health outcomes, including respiratory disorders, cardiac issues, and oncological risks. Chavan et al. emphasized the global burden of solid fuel use in developing countries, which is closely associated with high mortality from indoor air pollution. According to the World Health Organization, approximately 3.8\u0026nbsp;million deaths yearly are attributable to household air pollution, with solid fuel smoke among the top ten global risk factors\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Evidence across studies indicates co-exposure to PM2.5, VOCs, and PAHs indoors; risk appraisal should therefore adopt a multi-pollutant lens rather than a single-pollutant approach.\u003c/p\u003e\u003cp\u003ePoor ventilation substantially worsens indoor air quality by enabling the accumulation of toxic particulates. Maung et al.\u003csup\u003e73\u003c/sup\u003e demonstrated a direct correlation between inadequate airflow and elevated particle concentrations, a finding echoed by Lee et al.\u003csup\u003e74\u003c/sup\u003e who observed that prolonged exposure to such pollutants heightens the risk of respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Furthermore, Li et al.\u003csup\u003e75\u003c/sup\u003e highlighted that indoor combustion significantly increases acceptable particulate matter levels, compromising overall air quality. Notably, Maung et al. also reported that participants spend most of their time indoors, particularly during sleep, reinforcing the urgency of indoor air pollution control measures.\u003c/p\u003e\u003cp\u003eEmpirical evidence consistently supports the strong association between exposure to fine particles and volatile organic compounds (VOCs) at high concentrations and increased risks of asthma and other respiratory ailments. Research has also shown that homes in poor condition often lack adequate ventilation, exacerbating pollutant exposure from activities like cooking\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Chakraborty et al.\u003csup\u003e77\u003c/sup\u003e emphasized that individuals from lower socioeconomic backgrounds tend to spend more time indoors, increasing their risk of pollutant exposure. Children from low-income households living in poorly ventilated homes are especially vulnerable to adverse health impacts. High-density housing and low-quality building materials also contribute to pollutant accumulation and pose health risks to residents\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDomestic pollution, poor ventilation, and social inequality heighten environmental health risks. Therefore, a comprehensive public health strategy is essential. Key actions include improving building design and ventilation systems to mitigate exposure and protect susceptible populations.\u003c/p\u003e\n\u003ch3\u003e3) Hidden Risks from Soil, Dust, and Household Water\u003c/h3\u003e\n\u003cp\u003eExposure to contaminants through soil, dust, and household water remains an often overlooked public health risk in residential environments. Several studies have revealed high levels of heavy metal contamination in domestic settings due to anthropogenic activities. For instance, Jabbarov et al.\u003csup\u003e79\u003c/sup\u003e reported the presence of lead (Pb), zinc (Zn), and cadmium (Cd) around domestic waste areas and vehicle emissions; however, their data are more applicable to waste disposal zones than general residential neighborhoods.\u003c/p\u003e\u003cp\u003eAl-Swadi et al.\u003csup\u003e80\u003c/sup\u003e found that heavy metal contamination levels vary between urban and suburban areas, depending on proximity to industrial and mining activities. Additionally, dust pollution from industries such as cement production negatively affects soil microbial activity, disrupts biogeochemical cycles, and poses health risks. Specifically, untreated cement particles can alter the physicochemical properties of soil, reducing microbial populations and overall soil health\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. This reduction in microbial diversity correlates with nutrient cycle disruption, vital for soil fertility and agricultural productivity\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Dust also impairs soil moisture retention, exacerbating its impact on microbial dynamics and environmental health\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Consequently, dust pollution is a significant environmental issue affecting terrestrial ecosystems and public health through multiple soil quality\u0026ndash;related exposure pathways\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eContaminated drinking water due to pesticides and heavy metals is also a growing concern. El-Saeid et al.\u003csup\u003e84\u003c/sup\u003e, noted that polycyclic aromatic hydrocarbons (PAHs) from vehicle emissions can infiltrate soil and dust, posing health risks when inhaled. A study in Lebanon revealed contamination of water sources by various pesticides, with each concentration directly impacting drinking water quality\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Similarly, Bawa et al. estimated high health risks from consuming crops contaminated by heavy metals, underscoring the dangers of uncontrolled agricultural practices\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Furthermore, indiscriminate pesticide use has been linked to degraded water quality and hazardous ecological impacts due to pollutant accumulation in aquatic ecosystems\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. As observed in the Pearl River Delta region, the infiltration of PAHs from vehicle emissions into soil further worsens environmental health conditions\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e4) Geographic Gaps and Methodological Challenges in the Literature\u003c/h3\u003e\n\u003cp\u003eThe geographic distribution of studies highlights significant disparities, particularly regarding the Global South. Most studies originated from Asia (68%) and Africa (16%), revealing a stark geographic bias that hampers the generalization of global policy recommendations. This research concentration in relatively better-resourced regions aligns with findings in public administration literature, which show that wealthier areas receive greater academic attention while less affluent regions are marginalized\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. This pattern perpetuates a cycle of exclusion in which the voices and experiences of underrepresented communities are frequently ignored in global discourse\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. The evidence base is concentrated in Asia (68.2%) and Africa (15.9%), whereas Latin America \u0026amp; the Caribbean and Oceania each contribute only 2.3%; methodologically, experimental/field\u0026ndash;lab studies dominate (40.9%) and cohorts remain rare (4.5%). This distribution limits global generalizability and long-term causal inference and underscores the need for longitudinal designs and participatory approaches.\u003c/p\u003e\u003cp\u003eSuch bias impairs our understanding of local mitigation behaviors and the unique experiences of affected communities. The lack of community-based participatory approaches exacerbates the issue, even though such methods are essential for capturing contextual realities\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. While recent studies have underscored the need for diverse methodological frameworks\u0026mdash;including qualitative studies\u0026mdash;the literature remains dominated by quantitative experimental designs that fail to reflect community perspectives\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. This omission overlooks critical local insights and risks deepening disparities in representation and research outcomes.\u003c/p\u003e\u003cp\u003eThe consequences of this bias extend to funding priorities and the agenda-setting for environmental and social issues. Research from regions with higher academic capacity and funding tends to be prioritized\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. In contrast, Latin America and Oceania remain underrepresented despite their important ecological and social contexts for shaping global policy\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. This imbalance threatens the validity and applicability of scientific findings at the international level. Expanding the geographic scope and adopting qualitative methodologies are vital to reducing bias, improving policy relevance, and fostering a more inclusive understanding of challenges and solutions across global communities.\u003c/p\u003e\u003cp\u003eConceptually, these findings identify intervention entry points across a One Health triad\u0026mdash;encompassing people, the residential environment, and domestic practices\u0026mdash;with a focus on clean-fuel transitions, minimum ventilation, and risk communication related to household chemicals. Harmonizing cross-media metrics (air\u0026ndash;dust/soil\u0026ndash;water) is essential so that policy performance indicators can be tracked across places and over time.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAcross 44 studies, residential environmental health risk is dominated by indoor air pollution (25/44; 56.8%), followed by soil/dust contamination (10/44; 22.7%), household chemical exposure (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%). The evidence base is geographically concentrated in Asia (68.2%) and Africa (15.9%), with Latin America \u0026amp; the Caribbean and Oceania each contributing only 2.3%, limiting global generalizability. Methodologically, experimental/field\u0026ndash;lab designs prevail (40.9%), while cohort/longitudinal studies are rare (4.5%). Urban residents are most frequently examined (63.6%), and vulnerable groups include children (22.7%), older adults (6.8%), and low-income families (6.8%). The most analyzed endpoints\u0026mdash;heavy-metal concentrations (25.0%) and air quality (22.7%)\u0026mdash;indicate a multi-pathway but indoor-air-centric burden, with geographic and methodological gaps constraining long-term causal inference and equity-minded policy translation.\u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003ePolicy and practice should prioritize clean-fuel transitions, minimum ventilation/building standards, and targeted risk communication and management of household chemicals (clear labeling, safe storage, safer substitutes), with tailored emphasis on children, older adults, and low-income households in dense urban settings. Non-air pathways must be integrated via dust/soil management (scheduled wet cleaning, source control) and risk-based household water testing and treatment. To strengthen the evidence base, future work should expand to under-represented regions (Latin America \u0026amp; Oceania), adopt cohort/longitudinal and community-based participatory designs, and harmonize HRA/indicator metrics across air\u0026ndash;dust/soil\u0026ndash;water so findings are comparable across places and over time. All actions are best organized within a One Health framework to align health\u0026ndash;environment\u0026ndash;housing interventions from community practice to national policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eTrial registration number: Not applicable.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that there is no competing interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe work was not funded.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKholis Ernawati conceived and designed the study, conducted the literature search and data extraction, performed the analysis and interpretation, and wrote and revised the manuscript. The author has read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the anonymous peer reviewers for their insightful feedback and constructive suggestions. We also extend our sincere appreciation to all scholars whose published works were synthesized and cited in this article; their rigorous research provided the empirical and conceptual foundations of this review. Any remaining errors are our own\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePutri ANAR, Salam RA, Rachmawati LM, Ramadhan A, Adiwidya AS, Jalasena A, et al. Spatial Modelling of Indoor Air Pollution Distribution at Home. J Phys Conf Ser. 2022;2243(1):012072.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung C, Abdelaziz Mahmoud NS. Navigating dust storms and urban living: an analysis of particulate matter infiltration in Dubai\u0026rsquo;s residences. 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Location biases in ecological research on Australian terrestrial reptiles. Sci Rep. 2020;10(1):9691.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllison G, Jones M, Cain B, Bettridge CM. Taxonomic and geographic bias in 50 years of research on the behaviour and ecology of galagids. PLoS ONE. 2021;16(12):e0261379.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradford HM, Puhl RM, Phillippi JC, Dietrich MS, Neal JL. Implicit and Explicit Weight Bias among Midwives: Variations Across Demographic Characteristics. J Midwifery Womens Health. 2024;69(3):342\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"residential exposure, indoor air pollution, dust/soil, heavy metals, vulnerable populations, PRISMA, One Health","lastPublishedDoi":"10.21203/rs.3.rs-7994301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7994301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental health risks in homes are driven by indoor air pollution, contaminated dust/soil, and contaminated water, as well as the use of household chemicals, with unequal mitigation across different settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA PRISMA-guided Systematic Literature Review (SLR) synthesized 44 Scopus-indexed studies (Jan 2015–Mar 2025). We charted exposure types, research designs, populations, variables, and geography.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndoor air pollution was most frequent (25/44; 56.8%), followed by soil/dust (10/44; 22.7%), chemical exposure (4/44; 9.1%), water (3/44; 6.8%), and heat/climate stress (2/44; 4.5%). Studies were concentrated in Asia (68.2%) and Africa (15.9%); Latin America \u0026amp; the Caribbean, as well as Oceania, each accounted for 2.3%. Designs were dominated by experimental/field–lab studies (40.9%), with cohort/longitudinal studies being rare (4.5%). Populations most examined were urban residents (63.6%); vulnerable groups included children (22.7%), older adults (6.8%), and low-income families (6.8%). The most-analyzed variables were heavy metal concentration (25.0%) and air quality (22.7%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe residential risk burden is primarily linked to the use of solid fuels, poor ventilation, and household chemical practices, with apparent geographic and methodological gaps. Priorities include clean-fuel transition, ventilation upgrades, targeted risk communication on chemicals, and broader community-based research—framed within the One Health framework to integrate health, environment, and social actions.\u003c/p\u003e","manuscriptTitle":"Environmental Health Risks in Residential Settings: A Systematic Review of Indoor Air Pollution, Domestic Chemical Exposure, and Global Representation Inequities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 08:41:40","doi":"10.21203/rs.3.rs-7994301/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95856521-3f22-4de4-bc82-007b7efdd635","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-29T09:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 08:41:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7994301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7994301","identity":"rs-7994301","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}