Spatial Patterns of Breast Cancer Risk Associated with Industrial and Environmental Pollutants: A Scoping Review.

1. Introduction
Breast cancer is the most widespread malignant disease and the leading cause of cancer-related deaths among women worldwide [1,2]. According to the World Health Organization [3] (n.d.), more than 2.3 million new cases were reported globally in 2022, resulting in approximately 670,000 deaths. While incidence rates have risen in both developed and developing regions, survival outcomes remain less favorable in underdeveloped areas and among vulnerable women in developed regions [4]. Research over the past few years has identified several genetic and lifestyle risk factors including inherited changes in deoxyribonucleic acid (DNA) repair genes, including breast cancer gene 1 (BRCA1) and breast cancer gene 2 (BRCA2), obesity, alcohol consumption, reproductive history, and hormonal therapy use have been identified as contributing factors to breast cancer development [5,6]. However, growing evidence [7,8,9] links environmental chemical exposures to breast cancer and illustrates that while evidence on specific chemicals, genetic susceptibility, and timing of exposure exists, there remains a fragmented and inconsistent understanding of how industrial environmental exposures collectively influence breast cancer incidence. Industrial pollutants such as heavy metals, dioxins, and endocrine-disrupting chemicals often persist in the environment and affect populations living near industrial areas. Identifying these risks supports stronger environmental regulations and promotes health equity by protecting vulnerable communities from harmful exposures.
Industrialization has transformed ecosystems and human health through the release of a wide array of toxic substances into the environment [10]. The environmental exposure pathway concept illustrates how pollutants from industrial processes such as manufacturing, petrochemical refining, mining, and waste incineration can contaminate air, water, and soil, thereby entering human biological systems through inhalation, ingestion, or dermal absorption [11]. Among these pollutants, several are known or suspected carcinogens and endocrine-disrupting chemicals (EDCs), including benzene, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dioxins, and heavy metals such as cadmium and lead [12,13].
Chronic exposure to these carcinogens may influence breast cancer risk through multiple biological mechanisms [14]. EDCs can interfere with hormonal regulation, disrupting estrogen and progesterone pathways that regulate breast tissue development and cell proliferation, while genotoxic agents can induce DNA damage and oxidative stress leading to malignant transformation [15,16]. Nevertheless, establishing a definitive causal relationship between environmental and industrial exposures and breast cancer remains challenging. The disease’s long latency period, combined with varying exposure intensities, complex mixtures of pollutants, and differences in exposure assessment methods, creates uncertainty in existing epidemiological evidence [17,18]. These complexities highlight the need for a comprehensive synthesis of current research to explore environmental and industrial exposures, which may lead to an increased yet often overlooked risk in breast cancer etiology.
Building on the mechanistic understanding that highlights potential carcinogenic pathways, several studies have examined how environmental and industrial exposures vary geographically in relation to breast cancer incidence. Prior studies have reported associations between residential proximity to industrial air emissions or heavy-metal contamination and breast cancer incidence [19,20,21,22]. However, the evidence linking environmental and industrial pollutants to breast cancer remains inconsistent due to variation in exposure assessment methods, heterogeneity in study designs, and limited control for key confounding factors [23,24].
In addition, most spatial and industrial exposure studies examining breast cancer have been conducted in North America and Western Europe [25], while evidence from Africa, Latin America, and much of Asia remains limited. Few population-based studies in these regions have assessed industrial or environmental pollutant exposures in relation to breast cancer, largely due to limited cancer registry coverage and insufficient environmental exposure data [26,27]. This geographic imbalance highlights the fragmented and region-specific nature of existing research and underscores the need for a comprehensive synthesis to map global evidence on how industrial and environmental exposures shape spatial disparities in breast cancer risk.
Although the relationship between environmental exposures and cancer risk has been examined previously, the existing literature remains dispersed across different pollutant classes, geographic contexts, and methodological frameworks. Many studies focus on individual exposures or specific regions, making it difficult to assess broader patterns or compare findings across locations. Few reviews have systematically summarized this literature with attention to how environmental exposures are measured and reported across geographic contexts. This scoping review therefore aims to synthesize recent studies examining industrial and environmental pollutants and breast cancer, while identifying recurring patterns, regional gaps, and methodological limitations that affect interpretation and application of the evidence.
Given the methodological heterogeneity, inconsistency of reported findings and geographic imbalance of existing evidence, a scoping review is the most appropriate approach to synthesize the available evidence with the goal of mapping of the breadth, nature, and methodological diversity of studies spanning epidemiological and environmental science. This approach will identify key exposure types and measurement, study designs, regional patterns, and research gaps, thereby clarifying how industrial and environmental pollutants have been investigated in relation to geographical patterns of breast cancer risk. These insights are essential to inform environmental monitoring, urban planning, and exposure-reduction strategies aimed at cancer prevention. The aim of this review is to comprehensively map and synthesize global evidence on the relationship between environmental and industrial exposures and the geographic patterns of breast cancer incidence. Specifically, it will (1) describe the primary ambient exposures examined, (2) summarize geographic and methodological trends, and (3) identify research gaps and limitations in current study designs. Overall, the review will clarify the current state of knowledge, highlight methodological weaknesses, and propose priorities for future research.

2. Materials and Methods

2.1. Study Design and Framework
This review followed a scoping review design, which was used to map and summarize existing research on geographic and environmental factors related to breast cancer. The goal was to identify how different studies have examined these relationships and to describe the main patterns, methods, and research gaps in the field.
Studies used a variety of observational designs, including ecological, case–control, cohort, and cross-sectional studies. These studies varied in how exposures and outcomes were measured but were connected by their focus on spatial or environmental patterns and breast cancer. The scoping framework guided the review toward summarizing evidence and highlighting areas that require further investigation.

2.2. Research Questions
The review was designed to address five primary questions:What design methodologies are commonly used to examine environmental exposures and breast cancer risk?

What is the quality and nature of the evidence linking ambient exposures to breast cancer?

What specific environmental exposures are most frequently associated with breast cancer risk?

Which geographical locations have been most studied in this context?

Where are the research gaps?

2.3. Eligibility Criteria
Eligibility criteria for this study were structured using the PICOS (Population, Intervention, Comparator, Outcome, Study Characteristics) framework. (1) Population: included females (sex assigned at birth) of any age from any worldwide location; excluded males, studies without gender stratification, and animal studies; (2) Intervention: included endocrine disruptors, known carcinogens, toxic air pollutants, or ambient exposures; excluded biological factors, lifestyle factors (diet, alcohol), psychosocial stressors, medical treatments, or non-toxic environmental factors (light, noise pollution); (3) Comparator: no specific comparator required; (4) Outcome: included breast cancer, breast malignancy, breast carcinoma, or ductal carcinoma in situ (DCIS); excluded other cancer types, benign breast conditions, non-specific outcomes (all cancers combined), secondary outcomes unrelated to breast cancer (survival rates only), and screening or prevention-only studies; (5) Study Characteristics: included peer-reviewed articles published in English between 2015 and 2025 (with one eligible article published in French) that contained geographical or location-specific details and measured incidence, prevalence, or specific biological markers relevant to breast cancer.

2.4. Search Strategy
A comprehensive literature search was conducted across four electronic databases (PubMed, Scopus, Web of Science, and Embase). The strategy combined three core concept groups using Boolean operators: (1) breast cancer terms (e.g., “breast cancer,” “mammary carcinoma,” “breast neoplasm,” “ductal carcinoma,” “lobular carcinoma,” “triple-negative breast cancer,” “HER2-positive breast cancer”); (2) geography and spatial analysis terms (e.g., “geography,” “spatial analysis,” “geospatial analysis,” “spatial distribution,” “spatial epidemiology,” “GIS,” “geographical clustering,” “regional variation”); and (3) environmental exposure terms (e.g., “endocrine disruptor,” “endocrine disrupting chemicals,” “EDCs,” “environmental carcinogens,” “toxic air pollutants,” “ambient exposure,” “chemical exposure,” “chronic exposure”). Wildcard characters (*) were used where appropriate to capture variations in terminology. The search string was adapted for each database to accommodate indexing and syntax differences. Searches were limited to publications from 2015 onward, with no language restrictions. All results were exported into Covidence for deduplication and screening.

2.5. Study Selection
All retrieved records were imported into Covidence, where duplicate citations were automatically identified and removed. The study selection process followed PRISMA-ScR guidelines and involved two stages. During title and abstract screening, two independent reviewers screened all records against the predefined eligibility criteria using Covidence’s highlighting feature to identify keywords indicating inclusion or exclusion. Studies marked for inclusion by both reviewers advanced to full-text review. During full-text review, two reviewers independently assessed each article for final eligibility. Disagreements at both screening stages were resolved through discussion, and when consensus could not be reached, a third reviewer resolved the conflict.
Full-text exclusion reasons were customized in Covidence and included the following: (1) not peer reviewed; (2) wrong outcomes; (3) breast cancer not examined as an outcome; (4) wrong intervention; (5) not specific to breast cancer; (6) wrong study type; (7) examined biological or lifestyle factors; and (8) wrong exposure. The number of records identified, screened, excluded, and included at each stage was documented using a PRISMA-ScR flow diagram.

2.6. Quality Assessment
Although formal critical appraisal is not mandatory for scoping reviews, an assessment of methodological quality was undertaken to characterize the rigor of the existing evidence. Due to the heterogenous nature of these studies, an appraisal of methodological quality and risk of bias was conducted using a Joanna Briggs Institute (JBI)-aligned, domain-based framework adapted for observational environmental epidemiology studies (Table A1). The framework evaluated study design, population and sampling, exposure measurement, outcome measurement, confounding control, and data quality/bias. Each study was independently reviewed by two authors, and results were reached by consensus.

2.7. Data Extraction
Data extraction was completed using a structured form that was developed prior to the review to maintain consistency and accuracy across studies. Two reviewers extracted information independently, then compared results and resolved any differences through discussion. The process focused on capturing key study characteristics and outcomes that aligned with the objectives of the review.
The extraction form included fields for author, publication year, country, and study design, along with details about the study population such as sample size and geographic setting. Information on environmental exposures was collected, including the exposure type, specific pollutants measured, and data sources such as monitoring stations, satellite imagery, or Geospatial Information System-based models. Outcome data were recorded by association and study design. Reported effect estimates, confidence intervals, and p-values were included to capture the strength and direction of associations.
Potential confounding variables were noted, and any limitations or biases discussed by study authors were summarized. Extracted data were reviewed for completeness and accuracy before synthesis. All data management, reviewer notes, and consensus tracking were conducted within Covidence.

2.8. Data Synthesis
Data from the included studies were organized and analyzed in a spreadsheet using a structured coding matrix. Key information from each study was entered into the matrix, including study design, exposure type, region, and main findings. During the second stage of coding, studies were grouped and compared to identify common patterns in exposures, geographic focus, and reported associations with breast cancer.
Simple frequency counts and summaries were used to show how often certain designs, pollutants, or results appeared across the 51 studies. This approach helped highlight major trends, consistencies, and research gaps without conducting formal meta-analysis.

2.9. Ethical Considerations
This review used publicly available data and did not involve human participants; therefore, institutional ethical approval was not required.

2.10. Use of AI Tools
Limited assistance from artificial intelligence software tools was utilized during manuscript preparation. Specifically, ChatGPT (GPT-5.1, OpenAI) and Claude (Anthropic) were employed to support minor editing tasks, including grammar checking, sentence restructuring for clarity, and organizational suggestions for section flow. All substantive content, including study selection, data extraction, analysis, interpretation of findings, and scientific conclusions, was developed independently by the research team. AI-generated suggestions were reviewed and modified by the authors to ensure accuracy and alignment with the study’s objectives and findings.

3. Results

3.1. Selected Study Characteristics
In total, 560 studies were identified through the literature search. Following automatic removal of duplicates, 520 studies’ titles and abstracts were screened, with 415 studies excluded. A total of 105 full texts were screened for eligibility based on the inclusion criteria. After final study selection, 51 total studies were included for data extraction and analysis in this review (Figure 1). In total, exclusions were distributed as follows: 12 not peer reviewed, 12 wrong outcomes, 14 breast cancer not examined as an outcome, 7 wrong intervention, 4 wrong study type, 3 examined biological or lifestyle factors, and 2 for wrong exposure. Of the 51 included studies, case–control designs were most common (n = 22, 43%). The remaining studies employed ecological (n = 10, 20%), cohort (n = 9, 18%), cross-sectional (n = 6, 12%), and risk assessment or simulation study designs (n = 4, 8%) (Table 1). The predominance of retrospective designs reflects the feasibility of linking existing cancer registry data with environmental exposure estimates, though this approach limits temporal inference and causal determination.

3.2. Quality and Bias in Results
Overall, the quality of the studies included was high, and the risk of bias was low. Most studies clearly stated their design, inclusion, methods, outcomes, confounders, and data. Of the 51 studies included, 44 were classified as having an overall low risk of bias, with only four studies assessed as being high risk, while three studies’ risk of bias remained unclear. A summary of the risk of biased findings is presented in Figure 2.
The primary source of bias was related to data quality and bias control, with some bias in the lack of accounting for or controlling confounding factors. In contrast, the domains of study design, exposure, and outcome measurement exhibited the lowest risk of bias (Figure 2). These findings suggest that while the evidence base is growing, reporting and methodological rigor remain consistent in some domains but lacking in others.

3.3. Key Findings and Patterns
This section synthesizes patterns emerging from the reviewed studies, organized into five thematic areas: design methodologies, evidence quality, ambient exposure types, geographical trends, and research gaps. The findings highlight dominant approaches, recurring limitations, and uneven coverage across pollutants and regions.

3.3.1. Design Methodologies
Study designs ranged widely, reflecting differences in available data sources and research goals. Most of the studies employed case–control frameworks (n = 22, 43%), often linking environmental exposure indicators to individual cases drawn from cancer registries [19,29,40,67]. Several studies also used prospective cohort designs, allowing temporal assessment of exposures and outcomes [35,50,52,63,69]. Others relied on ecological or spatial analyses, using aggregated exposure measurements or municipality-level indicators [36,47,53,68]. Several studies used model-based or remote-sensing exposure approaches, integrating land-use regression, satellite-based observations, or atmospheric dispersion and chemistry-transport modeling [36,37,61,65,66]. Additional studies described descriptive or trend analyses to examine temporal patterns or spatial clustering of disease [34,48,59]. Finally, several studies combined elements of multiple designs, reflecting hybrid methodological strategies [24,34,39,62,63].

3.3.2. Evidence Quality and Nature of Evidence
Evidence quality evaluation included identification of sample sizes, environmental modeling techniques, stated biases, uncertainties, and overall significance of findings within the included papers. Many studies (n = 44, 86%) included more than 1000 breast cancer cases, providing adequate statistical power for detecting associations, while environmental modeling methods varied widely across analytic approaches. Modeled exposure datasets were acknowledged as useful but imperfect, with uncertainties related to spatial resolution, temporal averaging, and emissions inventory accuracy [36,37,61,65,69]. Silva 2019 relied on self-reported datasets and highlighted risks of recall bias, missingness, and exposure misclassification [67]. Investigations using national monitoring networks or remote sensing reported strong spatial coverage but noted inconsistencies across time periods and regions [32,35]. Ecological studies emphasized the difficulty of linking area-level exposures to individual outcomes [36,38,53,60]. Several cohort and case–control studies cited strong internal validity but limited generalizability beyond the populations studied [19,57,58,67].

3.3.3. Ambient Exposure Types Most Frequently Associate with Breast Cancer
Across the 51 studies reviewed, 67% (n = 34) reported statistically significant associations between environmental exposures and breast cancer risk (Table 2). An additional 21% (n = 11) reported mixed findings, with associations varying by breast cancer subtype (e.g., hormone receptor status), exposure window, pollutant concentration, or population subgroup. The remaining 12% (n = 6) reported no significant associations between pollutant exposures and breast cancer outcomes [28,40,42,45,47]. Pesticides showed 100 percent significance (n = 5), indicating consistent associations across all studies assessing agricultural, residential, or drift exposures [28,33,41,56,60,67]. PAHs also demonstrated 100 percent significance (n = 5), reinforcing strong links between polycyclic aromatic hydrocarbons and breast cancer risk [30,53,68,71]. PCBs showed 100 percent significance as well (n = 1), indicating a stable association within this small but consistent evidence base [43]. Traffic-related pollutants, particularly NOx, were significant in 80% (n = 4) of studies, further supporting the role of nitrogen dioxide and traffic emissions in breast cancer etiology [31,39,54,66]. Particulate matter showed 70% (n = 7) of papers with significance, reflecting consistent associations across PM2.5 and PM10 exposure windows [32,44,50,52,62,63,73]. Endocrine-disrupting chemicals had 50% of papers with significant findings (n = 8), with additional studies reporting mixed or inconclusive findings [58,59,61,69]. Findings for metals were more heterogeneous, with 40% (n = 5) showing significant findings, reflecting varying results across cadmium, arsenic, and mixed metal exposures [24,57]. Airborne dioxins papers showed 25% (n = 4), with additional studies producing non-significant or mixed findings depending on dose and exposure assessment [64]. Finally, pollutants were categorized as miscellaneous when studies did not specifically assess individual pollutants and instead focused on broad exposure groupings (e.g., “air quality” or “pesticides”), or when studies explicitly evaluated combined pollutant effects. For example, studies examining joint exposure to ethylene oxide and benzene reported higher breast cancer risk [38]. Studies assessing environmental exposures not typically included within major pollutant categories, such as geographic variation in radon exposure, were also classified as miscellaneous [70]. Overall, miscellaneous pollutants were found to be significant in 63% of studies (n = 8), emphasizing the importance of integrated exposures when assessing breast cancer risk [19,34,36,38,70]. Studies reporting mixed findings (n = 11, 21%) demonstrated the complexity of exposure-disease relationships in environmental breast cancer epidemiology. These studies typically identified significant associations for specific sub analyses—such as particular breast cancer subtypes (ER, HER2 status), exposure timing windows (childhood vs. adulthood), or high-exposure subgroups while finding null or inconsistent results in overall analyses.

3.3.4. Geographical Trends—Urbanicity, Region, Country, Hot Spot
Country-level classification revealed geographic patterns across the global research landscape (Figure 3). North American studies (n = 18), primarily from the United States and Canada, were well represented [24,35,39,45,46,59,61,72]. European studies (n = 18) were similarly prominent and included work from France, Italy, the United Kingdom, Spain, and Denmark [30,36,37,43,44,47,54,60,62,74]. South American studies were fewer in number but contributed important analyses from regions with agricultural expansion and industrial development [41,73]. Asian studies, including those from China, India, and Israel, examined rapid industrialization, dense urbanization, and PM-related exposures [52,58,61]. African representation was limited but included studies from Tunisia and Ethiopia [33,56]. A small number of studies incorporated multicontinental analyses, demonstrating broader international exposure patterns [36,37].
Industrial hotspot classification further differentiated studies based on whether authors explicitly designated regions as high-emission or high-intensity exposure zones. Some studies identifying clear industrial hotspots [46,61,67] while others noted the absence of hotspot conditions [39,40,41,44,74,75]. Collectively, the geographic classifications reveal substantial diversity across the literature, with wide variation in reporting practices and spatial detail. Regional analyses were well represented, while industrial hotspot and geographic specificity remain inconsistently reported across studies.

3.3.5. Stated Gaps
Across the dataset, several patterns emerged in the limitations reported by the studies. A dominant theme was the potential for exposure misclassification, often arising from modeled environmental exposures, reliance on ambient monitoring rather than personal measurements, or incomplete temporal alignment between exposure windows and disease development. Studies describing these challenges emphasized that exposure levels at residential locations may not fully reflect individual exposures due to mobility, commuting patterns, missing historical measurements, or broad geographic proxies [30,35,40,46,60,65,66,67,72,74,76,77].
A second major limitation theme involves incomplete covariates or individual-level data, including the absence of personal risk factors, behavioral exposures, reproductive history, or socioeconomic information. Studies noted that without these data, confounding could not be fully controlled, and risk estimates may be biased. This limitation was particularly common in ecological and registry-based designs, which rely on aggregated data rather than individual participant records [24,34,38,39,42,43,44,46,47]. Finally, some studies reported limitations in outcome measurement, including the inability to distinguish among breast cancer subtypes, limited staging data, or reliance on secondary outcomes such as mortality, density, or survival rather than incidence itself [34,45,57,60,72].

4. Discussion

4.1. Context and Interpretation
The role of environmental factors in breast cancer etiology has been hypothesized for decades yet remains inadequately integrated into prevention policy despite accumulating evidence. While mechanistic and toxicologic studies demonstrate the carcinogenic and endocrine-disrupting effects of environmental pollutants [78], translating these biological insights into population-level risk assessment and regulatory action remains challenging. This scoping review synthesized the current evidence base linking ambient environmental exposures to breast cancer risk, examining patterns of association, methodological approaches, evidence quality, geographic distribution, and research gaps across 51 studies published between 2015 and 2025.
Our synthesis reveals a paradox: while 67% (n = 34) of studies documented statistically significant associations between environmental pollutants and breast cancer risk, the evidence base struggles with limitations that constrain causal inference and policy translation. Research is geographically concentrated in high-income regions (71% from North America and Europe (n = 36), relies predominantly on retrospective observational designs (case–control, ecological, cross-sectional), and depends heavily on modeled rather than measured exposure data. Critically, only 10% (n = 5) of studies focused on industrial/chemical hotspot populations, who likely face the highest exposure burdens. These methodological and geographic constraints limit our ability to draw definitive causal conclusions or develop targeted interventions for the most vulnerable populations [79].

4.2. Methodological Factors (Limitations and Analytical Challenges)
Across the reviewed literature, the majority of studies reported statistically significant positive associations between environmental or industrial exposures and breast cancer incidence. However, the direction and magnitude of associations varied by pollutant class, exposure duration, and regional context. These heterogeneous results likely reflect differences in exposure characterization, population susceptibility, and study design [7,9,80].
The predominance of case–control found during this review is consistent with the historical dominance of retrospective epidemiologic methods in environmental health research. The reliance on case–control design likely reflects data availability and cost-efficiency but also risks items already discussed like exposure misclassification, temporal ambiguity and confounding [81,82,83,84,85].
Temporally, research activity in this area has accelerated in recent years. More than half of the included studies (n = 27, 53%) were published between 2021 and 2024, compared to 24 studies (47%) published between 2015 and 2020. This increasing publication trend suggests growing scientific recognition of environmental exposures as potentially modifiable risk factors warranting investigation for breast cancer prevention [86].
Reflecting such limitations of macro-level study design and the commonly cited limitations among the literature reviewed included potential exposure misclassification, unmeasured confounding, and ecological fallacy [87,88]. While most studies adjusted for major individual-level confounders such as age, smoking, occupation, and genetic history, others such as socioeconomic status and race/ethnicity were inconsistently addressed. The absence of standardized approaches to confounder selection and limited data on residential mobility further challenges the internal validity of many studies [86,89].
Interpretation of associations between environmental and industrial exposures and breast cancer risk is further limited by inconsistent adjustment for established risk factors across studies. Many studies included in this review adjusted for only a small number of individual-level variables, while others relied on area-level measures or did not report detailed covariate information. As a result, residual confounding by known breast cancer risk factors such as body mass index, reproductive history, and hormonal factors cannot be excluded. This limitation is particularly relevant for studies of endocrine-disrupting chemicals, given their potential to act through hormonal pathways that may overlap with metabolic and reproductive processes. Few studies in this review evaluated these factors jointly or examined whether associations varied across subgroups, which may contribute to null, inverse, or heterogenous findings in the literature.
Importantly, the focus on evaluation of pesticides and endocrine-disrupting chemicals and particulate matter reflects both their established biological plausibility and the relative availability of monitoring data [90]. These patterns may suggest that research attention has concentrated on well-characterized pollutants rather than the full spectrum of relevant exposures [91]. Although these pollutants may be better-characterized, emerging interest in mixed industrial exposures suggests a shifting research focus toward complex mixtures [91], yet consensus is still lacking their singular influences.
To detect pollutants, many studies relied on modeled or secondary exposure data sources, such as land-use regression (LUR) or ground monitoring, with only a small subset using direct or biomonitoring-based exposure assessment. Although LUR models have developed substantially over the past 2 decades and have a pivotal role in evaluating long- and short-term exposures [92], their assumptions and resolutions vary widely across settings, complicating cross-study comparability [80]. Dependence on modeled exposures, coupled with limited individual pathology, may obscure true dose–response patterns [93,94]. This methodological gap raises concerns about exposure misclassification and highlights the need for temporal and spatial resolution in future studies [95,96].
Analytically, integrating spatial and temporal modeling could improve precision, yet substantial variability persists in how exposures are defined, scaled, and temporally aligned with outcome data [97]. Of interest, several studies showed no association or significance between environmental pollutants and breast cancer when geographical information systems (GIS) spatial analysis was used alone. While GIS has added significant value to the world of environmental health research [98], it may be more suitable in conjunction with other methods to assess complex relationships between the combination of environmental pollutants and breast cancer. Taken together, the reviewed evidence brings to light a paradox in environmental epidemiology: as analytic sophistication increases, comparability across studies often decreases [99].

4.3. Geographic Disparities and Environmental Justice
Beyond methodological constraints, the distribution of the evidence itself is geographically skewed, raising issues of representativeness, and global generalizability. With many studies conducted in North America and Europe, the concentration in high-resource settings limits the external validity of findings and overlooks environmental exposures unique to industrializing settings [100]. Emerging work from Asia and South America contributes to valuable diversity, but studies from Africa and other low-income regions remain sparse. The absence of exposure monitoring infrastructure in low-income regions could perpetuate environmental inequity. This pattern mirrors broader inequalities in health research capacity, which in turn results in underserved populations bearing disproportionate exposure burdens without epidemiologic oversight needed to inform prevention.

4.4. Future Directions
The review underscores several priorities for strengthening epidemiologic evidence on environmental exposures and breast cancer risk. Firstly, advancing study design remains critical. The limitations in study design are reflected in the fact that most studies were retrospective in nature, including case–control and retrospective cohort approaches, with only a small number using prospective designs. While prospective studies cannot establish causality, they can establish temporality, clarify the sequence of exposure to outcome, and provide insight into the natural history of disease.
Improving exposure characterization is another essential step. Incorporating high-resolution exposure metrics, standardized pollutant groupings, and approaches capable of addressing multiple pollutants simultaneously will enhance comparability across studies. Integrating geographic information systems (GIS), land-use regression, and satellite-based remote sensing can provide more spatially refined and temporally resolved exposure estimates [95,96].
The current inconsistencies that exist among the literature, including variable exposure definitions and measurement metrics, confounder adjustments, and temporal alignment, hinder meta-analysis and adequate estimations of global risk burden. Emerging interest in prospective studies exploring combinations of environmental pollutants and breast cancer incidence are more closely aligned with real-world conditions of human exposure [101,102] and utilizing these methodologies going forward would strengthen temporal inference. The current variability and heterogeneity of cancer risk association studies reflect the difficulty of identifying the cause and effect of a longitudinal and biologically complex process at a geographically large scale.
Finally, expanding these implementations equitably into under-represented areas and vulnerable populations is critical to environmental justice [103]. Collectively, these efforts may facilitate the transition from correlation to causal inference and from hypothesis to actionable evidence.

5. Policy and Implications
The findings from this review point to a clear need for stronger policies that focus on prevention, even when full causal proof has not yet been established. Many studies indicate meaningful health impacts from pollutant exposure, especially endocrine-disrupting chemicals and heavy metals. A precautionary approach is warranted, since waiting for definitive causal evidence may allow avoidable harm. Strengthening air toxics standards, improving emissions reporting, and expanding monitoring of high-risk pollutants would better align environmental regulation with current scientific evidence [104,105].

5.1. Research Funding and Infrastructure
More investment is needed to support research that can clarify exposure pathways and strengthen causal assessment. Longitudinal and nested designs, biomonitoring, spatial exposure modeling, and multi-sensor data collection are essential for improving exposure precision and identifying sensitive life stages. These studies are resource-intensive but foundational for prevention. As an example, federal initiatives such as the NIH’s Environmental influences on Child Health Outcomes (ECHO) Program demonstrate how continuous investment in longitudinal cohorts, biomonitoring, and harmonized exposure assessment can strengthen causal interference and improve understanding of environmental influence on health and disease throughout the life course [106,107]. Expanding funding for long-term cohorts, environmental sensor networks, and community-based data collection may improve understanding of environmental burdens and guide policies that can prevent disease more effectively.

5.2. Advancing Health Equity and Public Health Prevention
Policy and research efforts should place health equity at the center of environmental health action. Increasing monitoring in underserved communities, supporting community-driven prevention programs, and investing in public health education can reduce disparities in exposure and disease risk [108]. Equity-focused policies such as the European Union’s Zero Pollution Action Plan 2030, whose implementation includes explicit attention to reducing social inequalities in pollutant exposures and ensuring that pollution control measures benefit disadvantaged and vulnerable populations [109,110]. Integrating environmental justice principles into this framework aims to mitigate unequal exposure burdens and align regulatory action with public health equity. Clear communication about environmental hazards, mitigation strategies, and early detection can empower affected communities. Integrating these efforts into existing public health systems, including health departments, primary care, and schools, can ensure that prevention strategies reach populations that need them most.

6. Conclusions
This scoping review synthesizes evidence from 51 studies published between 2015 and 2025, which revealed that 67% of studies identified statistically significant associations between environmental pollutants and breast cancer incidence, with particularly strong evidence for pesticides, PAH, PCB, NOx, and PM.
Despite this accumulating evidence, critical limitations constrain translation into public health prevention. The evidence base is geographically skewed towards North America and Europe, leaving substantial gaps in rapidly industrializing regions of Asia, Africa, and Latin America, where environmental burdens may be high. Methodologically, the predominance of retrospective design limits causal inference, while reliance on modeled exposure data introduces measurement error and potential misclassification.
Future research must address current limitations through prospective cohort studies with repeated biomonitoring across the life course, standardized exposure metrics enabling meta-analysis, multipollutant mixture models reflecting real-world exposures, and targeted investigation of industrial hotspot populations and underserved communities. Methodological concurrence is essential to enable pooled analyses and global risk estimation.
The disconnect between scientific evidence and regulatory response reflects a challenge in implementing the precautionary principle in environmental health policy. While many studies demonstrate that significant associations and biological mechanisms are well-established, the burden of proof needs to shift toward prevention rather than demanding absolute causal certainty before action. Strengthening air quality standards to account for cancer risk, expanding emissions monitoring in residential areas, integrating environmental exposure data into cancer surveillance systems, and enforcing protective buffer zones near industrial facilities represent evidence-based interventions that could reduce population exposure burdens.