Abstract
Objective:
To conduct a systematic review of the evidence regarding the impact of environmental health factors, such as air pollution, thermal stress, water and soil pollution, agricultural instability, and occupational hazards, on seasonal migration in China.
Methods:
A systematic review of PubMed, Web of Science, CNKI, Scopus, and Google Scholar (2001–2025) was conducted to identify peer-reviewed articles and institutional reports on environmental exposure, health, and migration outcomes in the Chinese mainland’s populations.
Results:
Outmigration trends were found to be associated with each of the four aspects of air pollution, heat stress, water contamination, and agricultural instability during the winter, and, in addition to the above, water and soil contamination, in the summer. The effects were found to be strongest among rural poor and older individuals. At urban destinations, migrants were also at risk of occupational hazards due to silica dust and chemicals.
Conclusion:
The environment exerts a multidimensional, seasonal influence on internal migration in China. There is a need for coordinated environmental health governance to minimize migrant exposure and promote health equity in this population.
Introduction
China has the largest internal migration system in the world. By 2022, internal migrants [] represented a floating population (internal migrants living outside their place of household registration) that had increased to more than 380 million, approximately 27% of the inhabitants, up more than a factor of 30 since 1980 (Figure 1) [, ]. Many migrants come and go on an annual or cyclical basis from their rural homes to urban destinations. Previous research on the wage gap between coastal and inland regions has suggested that this is the main factor that influences migration; however, increasing evidence suggests that environmental health factors also have a direct influence on migration, independent of the wage differential [, ]. This is supported by studies of urban growth patterns, industrial pollution siting, environmental policy and trade, and pediatric environmental burden [–]. Winter air quality often exceeds the World Health Organization (WHO) safety standards by more than twelvefold in many northern cities []; groundwater and soil are highly contaminated with heavy metals in many rural regions [, ]; and heat waves, floods, and droughts threaten crop production, causing heat stress for agricultural workers [, ].
FIGURE 1
Migration is influenced by environmental conditions in two connected ways: through environmental degradation affecting health and therefore labor productivity (direct pathway), and through environmental degradation affecting rural livelihoods and resulting in an economic push to migrate (indirect pathway) []. In China, seasonal migration tends to follow the same patterns as environmental stress, such as air pollution in winter, heat in summer, and pesticide use in spring.
The objectives of this review are to outline the main environmental health stressors to which migrants are exposed in China before and after migration; to summarize health outcomes related to these stressors; and finally, to highlight gaps in the research and policy implications. Using terminology from the literature, the floating population is defined as all the individuals who live outside their household registration (hukou) area; the seasonal or cyclical migrants are a subset of the floating population who move and return once a year, typically in response to a particular seasonal, climatic, or construction-related event; and environmental migrants are those whose moves are significantly influenced by exposure to the environment and the resulting impact on their livelihoods. The annual migration of older individuals to Hainan for climate and health reasons has been well documented in China as an example of environmentally initiated seasonal migration [–], and is representative of the pattern of seasonal migration that is the subject of this review at the national scale.
Previous studies have reviewed the relationship between climate migration in general [, , ], occupational hazards [], and water and soil pollution []. However, no previous studies have considered air quality, thermal stress, water pollution, soil pollution, and agricultural instability within a single framework, or have tied these stressors to the seasons of migration. In this review, these gaps are addressed by integrating five environmental domains into one analytical framework and mapping their seasonal timing (see Results). The broader theoretical framework on climate vulnerability migration, transnational patterns of climate-driven migration, and decision-making on migration in the absence of certainty also supports the approach followed here [–].
Methods
Literature search and eligibility criteria
A systematic search was carried out in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Scopus, and Google Scholar between 1 January 2025 and 30 May 2025, using terms related to internal migration (e.g., “floating population,” “hukou,” “rural-to-urban migration,” “seasonal migration”), environmental exposure (e.g., “air pollution,” “PM2.5,” “heat stress,” “water contamination,” “soil pollution,” “pesticide exposure”) and health outcomes (e.g., “respiratory disease,” “occupational injury,” “chronic kidney disease,” “mortality”). A manual search of the reference lists of the retrieved studies was undertaken to identify any further relevant studies.
Studies were eligible if the study population lived in mainland China; at least one environmental exposure was quantified (e.g., exposure to PM2.5 that exceeded the World Health Organization guidelines, or temperatures above 35 °C, or concentrations of heavy metals above Chinese national safety standards); at least one health or migration-related outcome was reported (e.g., mortality, disease incidence, biomarker levels, out-migration rate, or timing of seasonal departure); and the study was published in a peer-reviewed journal or official government report between 1 January 2001 and 30 May 2025. Studies of laboratory animals, or in vitro studies; studies lacking original data (e.g., reviews, editorials, or conference abstracts); studies conducted in populations other than mainland China (such as international migrants or non-mainland populations); and articles not available in full text were not included.
Study selection
Approximately 1,200 records were retrieved in the first database search. After full-text review, 75 studies were excluded (no original data, non-mainland population, or no clear health or migration outcome) and 75 met the eligibility criteria due to a lack of original data, a non-mainland Chinese population, or a lack of a clear health or migration outcome. Forty-one more sources were found by manually searching the reference lists giving a total of 116 included studies and reports (75 + 41). Finally, after a synthesis guided by the PRISMA 2020 reporting guidelines [], 116 peer-reviewed studies and institutional reports published between 2001 and 2025 were included (Figure 2). This systematic review was not prospectively registered.
FIGURE 2
Quality appraisal
A modified version of the Newcastle-Ottawa Scale (NOS) [] was used for the appraisal of the quality of the articles, which included cohort and cross-sectional studies. We also used the JBI Critical Appraisal Checklist for descriptive studies [, ] for three domains: selection of participants; comparability of groups; and quality of outcome measurement. No study was excluded due to quality alone, and higher-quality studies were given more weight in the qualitative synthesis. A formal risk-of-bias assessment was not conducted, as no single standardized instrument could be used uniformly across the 116 studies included, due to the epidemiological cohorts, household surveys, and government reports included having a variety of study designs and outcome definitions (see Limitations).
Analytical framework
The synthesis is structured around five areas of environmental health: air quality, thermal stress, water and soil contamination, agricultural instability, and occupational hazards at migration destinations. A temporal perspective was used to explore each domain by separating exposures experienced in rural places from exposures in urban destinations, thereby understanding how migration itself can buffer or mediate environmental health risks. This was followed by a cross-cutting analysis on the influence of gender, age, and socioeconomic status on exposure and mobility in each of the five domains.
In the Results section, direct evidence refers to studies that measure the behavior of migration itself, while indirect evidence refers to studies that measure the environmental impact on health, agriculture, or income, but not migration. When evidence was indirect, causal language was avoided, and terms such as “associated with,” “may contribute to,” or “may affect migration decisions” were used to clearly indicate the strength of the underlying evidence to the reader.
Results
Historical trajectory: environment and migration, 1980–2022
The relationship between environment and migration in China can be divided into three general stages. Stage 1 (1980-2000): first wave of rural-to-urban migration, industrialization and coastal pollution. Stage 2 (2001-2015): rapid growth of the floating population and pollution spreading inland; Jan 2013 PM2.5 episode and 2014 soil survey as turning points. Stage 3 (since 2016): improving urban air quality alongside rising thermal stress, water scarcity and climate pressure on agriculture. End the paragraph by stating that air-quality gains have been accompanied by growing pressure from other stressors. The first wave of rural-to-urban migration, which more than doubled the floating population between 1980 and 2000, from under 10 million to approximately 100 million, was accompanied by rapid industrialization and worsening air and water pollution in coastal regions, with the Yellow River drying up in 1997. Pollution sources and exposure shifted geographically from coastal to inland regions between 2001 and 2015, while the floating population increased by more than 250 million, with the PM2.5 concentration in Beijing reaching 900 μg per cubic meter in January 2013: this was a turning point in raising awareness at the public and policy levels. In the 2014 national soil survey, 16.1% of the farmland failed quality standards. Urban air quality has improved significantly since 2016, as PM2.5 levels dropped 30%–35% across major cities from 2014 to 2020 under the War on Pollution. Meanwhile, thermal stress and water scarcity have increased, especially in the central and western provinces, and are now significant drivers of migration, as is the impact of climate disruption on agriculture, which has been an increasing force particularly since 2018. As of May 2022, the floating population was more than 380 million. This trajectory indicates that as one environmental stressor (air pollution) is managed through specific policy, other stressors tend to emerge or intensify, although not necessarily to the same degree. (“The path” = the historical trajectory described above.)
Air quality as a driver of seasonal migration
Some of the most well-documented environmental health risks in China include fine particulate matter (PM2.5), nitrogen dioxide, and ground-level ozone. In 2017, approximately 1.24 million deaths in China were attributable to outdoor particulate pollution, or 29% of global deaths []. Air pollution is very seasonal, with winter coal heating causing high PM2.5 concentrations (up to 300 μg per cubic meter) in cities such as Beijing, Shijiazhuang, and Harbin, at a level nearly 20 times higher than the WHO 2021 24-h PM2.5 guideline is 15 μg per cubic meter). In the summer, strong sunlight and vehicle exhaust emissions lead to high ozone concentrations []. There are approximately 175 million rural households using solid fuels for cooking and heating, which causes indoor air pollution [], and systematic national exposure datasets support this estimate [, ].
Long-term exposure to PM2.5 is linked to an increased risk of cardiovascular disease, stroke, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections [–]. In 272 cities in China, an increase of 10 μg per cubic meter in PM2.5 was associated with a 0.22% increase in daily all-cause mortality [], and children in high-pollution areas have reduced lung function and increased asthma rates []. The estimated impact of indoor solid-fuel use on deaths in 2013 was approximately 900,000 deaths (5.7% of all-cause mortality) [], and the estimated difference in life expectancy between the north and the south of the country is approximately 1.8 years (Figure 3) []. Other Chinese and international cohort studies also provided supporting data on death rates, depression risk, diabetes incidence, and cardiometabolic outcomes due to PM2.5 [–].
FIGURE 3
There has been an increase in evidence on the direct impact of air pollution on migration decisions since 2015. One study on migration and ambient PM2.5 exposure using the Chinese General Social Survey revealed that higher exposure to PM2.5 in one’s place of origin (PPO) was linked to increased migration, independent of income, and was highest during the winter heating season []. In the industrial regions of Hebei Province, qualitative fieldwork revealed that mothers commonly moved temporarily with their children in winter due to respiratory health problems []. This air pollution-migration relationship is supported by a large body of empirical literature that shows that air pollution is negatively correlated with settlement intention and/or choice of destination in national surveys, in city-pair studies, and in push-pull frameworks [–77]. Pollution-driven migration is distinct from labor-market-driven migration in that it is not seasonal but is triggered by air quality episodes. Additionally, this specific type of migration is skewed toward caregivers and children.
Thermal stress and extreme temperature events
Since 1960, average temperatures in China have increased by 0.22 °C–0.33 °C per decade, which is twice the global average of 0.79 °C per decade [], and heat wave frequency has increased by approximately 60% over the last 20 years, and the number of heat-related deaths has also increased. By 2050, climate projections indicate that there will be 10–20 days per year with physiologically hazardous outdoor conditions in the major cities of China [78].
Heat exhaustion, heat stroke, dehydration, and the exacerbation of cardiovascular, renal, and respiratory disease occur at extreme temperatures [, 79]. Outdoor workers are most at risk (especially farmers and construction workers), and rice and wheat farmers in the Yangtze River Delta and in the North China Plain work more than 6 h outdoors each day during peak heat [80], which has been associated with an increased prevalence of chronic kidney disease among agricultural workers [81]. In fact, historically, the number of deaths from cold-related causes in 66 Chinese cities has been higher than the number of deaths from heat-related causes [82]. Similarly, other surveillance evidence has reported a greater number of cold-related deaths compared to heat-related deaths [83] in less well-insulated homes in the central and southern regions of China.
Heat stress has direct (worker health) and indirect (crop yield) impacts on migration. The potential loss of wheat yield is approximately 3%–8% for every 1 °C increase above the optimal temperature occurring during flowering and grain filling. Rice in southern China is similarly sensitive [84]. Households that rely on small-scale farming face pressure to turn to other sources of income when agricultural income drops, and out-migration tends to occur in the period between the harvesting of the previous crop and before planting the next one, which roughly corresponds to the agricultural year [].
Water and soil contamination
Nitrate contamination is the most common contaminant in the water, affecting more than 60% of monitored wells in northern China; arsenic [85] is the most common contaminant in drinking water, affecting an estimated 19.6 million individuals, primarily in Inner Mongolia, Shanxi, and Xinjiang []. Approximately 14% of river sections and 25% of lake monitoring points did not meet safe contact standards in 2020 [86].
In 2014, a nationwide soil survey revealed that 16.1% of agricultural soils were outside of the acceptable range [87] (Figure 4), mainly due to cadmium in rice in Hunan Province. Cadmium exposure can lead to kidney damage, poor bone density, and is associated with an increased risk of certain cancers [88]. Children living close to e-waste recycling centers in Guangdong and Zhejiang have higher blood lead levels; in Guiyu, the average child’s blood lead level is nearly three times the international guideline of 10 μg per deciliter [89].
FIGURE 4
Water scarcity exacerbates such contamination issues, especially in the north and northwest of the country. Over 150 million individuals in the Yellow River basin are increasingly facing water scarcity problems due to overexploitation, reduced glacial melt, and increased evaporation [90]. Household water insecurity has been linked to poor sanitation, diarrhea, decreased food quality, and psychosocial stress, which can encourage outmigration [91]; water shortages before the spring monsoon are an important factor in determining the timing of migration from arid areas [91].
Climate-related agricultural instability and occupational hazards at migration destinations
Farming systems are becoming more fragile in response to warming and increasing flood and drought frequency, while technological progress has not been able to improve wheat and maize production in northern provinces since the 1980s [84, 92]. The relationship between migration decisions and the variability in rainfall within a single agricultural season appears to be more significant than the relationship with long-term rainfall averages: the occurrence of an extreme rainfall event in a given agricultural season is correlated with an increased likelihood of outmigration in the subsequent season []. In addition, the use of pesticides in China is very high, and China is the world’s biggest producer and consumer of pesticides; occupational, prenatal, and childhood exposure to pesticides has been associated with neurological impairment. Migration rates are statistically higher in areas of northern and northwestern China affected by desertification than in other areas [93], and areas with severe land degradation, such as the Loess Plateau, Inner Mongolia, and the Tarim Basin, are also impacted by desertification [94].
Migration does not take the environmental health risk away; it just moves it. Construction activity occurs in the spring and summer, when the largest number of seasonal migrant workers (SMWs) are in the country, and this activity is estimated to expose 50 million workers to silica dust (silicosis) exposure, noise-induced hearing loss (NIHL), and heat exposure []. Exposure to organic solvents, heavy metals, noise, and repetitive strain is common among manufacturing workers in the Pearl River and Yangtze River deltas, and peripheral nerve damage has been linked to solvent exposure in workers at Dongguan e-waste factories, with more than 40% of workers having more than 3 years of experience in the industry exhibiting noise-induced hearing loss []. Although the hukou system has been reformed since 2014, migrants’ access to local healthcare, schooling, and social insurance coverage is still limited, and informal workers are not sufficiently covered [, 95]. In Guangdong, the proportion of construction migrants who were able to identify hazards associated with silica dust was less than 30%, and less than half were able to use the respirators provided [].
Cross-cutting and moderating factors
Gender, age, and socioeconomic status are important determinants of exposure and the ability to react to it through migration.
Rural women suffer more from indoor air pollution because they cook and do housework. They also face higher barriers to migration because of childcare responsibilities, while male migrants are engaged in agricultural and pesticide-related tasks in the countryside. Both children and older adults are more vulnerable to the effects of pollutants [] and are less likely to migrate, as evidenced by a large body of literature on age-graded migration [96–107]. Higher-income households have more resources to move and may also have access to protective measures such as water filters, whereas lower-income households are less likely to have the resources to protect themselves locally or to move [], which can be referred to as a poverty-environment trap; there is also some evidence regarding whether higher-income households are more likely to participate in welfare programs and whether migrants have social insurance coverage [108–110]. These factors do not act alone but are interrelated: A socioeconomic factor influences exposure and mobility; gender influences exposure through household roles; and age influences mobility directly (Table 1).
TABLE 1
| Population group | Primary environmental exposures | Key health outcomes | Migration pattern and barriers |
|---|---|---|---|
| Women in rural settings | Indoor solid fuel smoke; pesticide residues; lead, cadmium | Anemia; adverse birth outcomes; chronic respiratory disease | High environmental burden; limited mobility due to caregiving responsibilities |
| Children under 5 | PM2.5; lead; pesticides; contaminated water | Impaired lung development; neurotoxicity; stunted growth | Moved by caregivers during pollution peaks; otherwise remain in high-risk areas |
| Older adults in rural settings | Cold stress; indoor coal smoke; arsenic or fluoride in water | COPD; cardiovascular events; renal dysfunction | Lowest mobility; isolated in degraded areas when working-age adults migrate |
| Agricultural workers [–, 116] | Extreme heat during harvest; pesticide exposure; dust | Heat illness; chronic kidney disease; organophosphate poisoning | High mobility; seasonal circular migration between farming and construction or factory work |
| Construction migrants [–, 116] | Silica dust; extreme heat; noise; solvents | Silicosis; COPD; hearing loss; solvent neuropathy | Migrate to urban destinations; occupational exposures add to burdens from their places of origin |
| Pregnant women | Pesticides; heavy metals; air pollution | Preterm birth; low birth weight; neurodevelopment deficits in newborns | Some migration to escape pollution peaks pollution peaks; access to prenatal care often blocked by the hukou system |
Environmental health exposures, health outcomes, and migration patterns by vulnerable population group. Systematic review, China, 2001–2025.
These five environmental domains also interact with each other and should not be examined individually. In the north, winter air pollution occurs in the same households as cold-exposure-related health risks; at the same time, in the same season, a combination of PM2.5 and heat stress during the summer season could be more harmful than either stressor individually. In rural areas, water scarcity is often coupled with insecure agriculture: when agricultural production is reduced due to drought, so is rural income, which in turn reduces water supply. In the places they travel to, individuals who already have health problems from the heavy metals or pesticides to which they were exposed in their own country may be exposed to more silica dust or chemicals at work sites or in factories. Community-level social capital, place attachment, and intergenerational solidarity also influence the experience and reaction of migrant populations to these combined exposures [111–115].
Seasonal patterns of environmental stress
Air pollution is highest in winter when individuals are using home heating; heat stress is highest in the summer; water scarcity is highest in the spring before the monsoon season; pesticide exposure is highest from spring through summer, when crops are being planted and grown; and agricultural disruption and outmigration are highest in the fall and early winter, after the harvest season (Figure 5).
FIGURE 5
This results in an overall foreseeable migration pattern: outmigration in autumn after harvest, with increasing air pollution and decreasing demand for farm labor; return migration in spring when land is being prepared.
Discussion
Policy implications
Although there have been measurable improvements in some areas, such as the 2014 Air Pollution Action Plan, partial hukou reform, and the 2015 extension of pollution control to soil and water, environmental health governance for migrants is still underdeveloped, and the “Healthy China 2030” initiative lacks a focus on environmental migrants.
At the national level, there is potential to connect environmental monitoring data to migrant registration to monitor the health effects of environmental migration, and there is potential for more reform in the hukou system to enhance migrants’ access to healthcare, especially for occupational diseases. High pollution and climate-sensitive hotspots require increased occupational health safeguards at migration destinations, with mandatory health screening for construction workers, heat illness prevention regulations, and a wider spread of occupational health clinics, incentivizing construction workers in high-inflow provinces (including Guangdong, Zhejiang, Jiangsu, and Beijing). A seasonal response could be achieved at the community level through the use of low-cost air and water sensors, real-time digital air and water alerts, and culturally appropriate health education on topics such as air pollution, heat illness, pesticide safety, and water testing.
Specific evidence-based measures include coordinated air quality alerts during the November-March heating season in cities whose pollution levels are above 100 μg per cubic meter of PM2.5; subsidized respiratory screenings in high pollution areas; targeted risk communication in outsourcing communities about protective measures; mandatory rest breaks and heat protection rules for outdoor workers; climate-resilient agricultural support and crop insurance in high outmigration counties; and accelerated groundwater remediation with targeted blood lead screening in provinces with known contamination such as Hunan and Guangdong. These recommendations are based directly on the exposure pathways and populations listed above, and are more specific than policy goals, moving toward those interventions that can be implemented and evaluated.
Limitations
There are some gaps in the current evidence base. Longitudinal, household-level studies of exposure, health, and migration over time are limited, with the majority of studies being cross-sectional and area-level, hence not able to isolate environmental factors from economic factors of migration at the individual level. These also cannot draw conclusions about causality. Environmental and environmental-economic motivations are also very often grouped in the same household decision, which makes the distinction between these motivations difficult to assess in the available data. Publication bias may further work against studies that do not report a clear environment-migration link. The definitions, exposure measures, and geographic scope differ extensively between studies, making direct comparisons difficult, and only a few studies measure seasonal migration as a primary outcome; rather, they extract its relevance from data gathered for other aims.
Due to the heterogeneity of study designs, exposure measures, and definitions of outcomes across the 116 sources included, a formal, standardized risk-of-bias assessment was not possible. This is recognized as a limitation that restricts the capacity for formal weighting of the underlying evidence’s relative strength; future domain-specific systematic reviews based on this framework should use an appropriate and structured tool for the study design–ROBINS-I for non-randomized studies, Cochrane RoB 2 for controlled trials, and the Newcastle-Ottawa Scale for cohort and case-control studies.
Other research priorities for future studies include the health effects that may result from combined and simultaneous exposures (e.g., combined exposure to PM2.5 and heat, or exposure to pesticides and contaminated water), which remain under-researched among Chinese migrant populations; the degree to which occupational risk factors faced at the destination site add to the health impact of the environmental exposure that was experienced at the point of origin; and the analysis of the motives for environmental migration and health impacts disaggregated by gender, since gender is clearly a determinant of exposure and the ability to migrate.
Conclusion
This review synthesizes evidence from air quality, thermal stress, water pollution and soil pollution, agricultural instability, and occupational hazards to demonstrate that environmental conditions in China are a multi-dimensional, seasonally organized driver of population movement. Air pollution appears to influence out-migration from the north of China the most; thermal stress and agricultural disruption drive migration from farming areas; and heavy-metal contamination drives migration from industrial hotspots. Gender, age, and socio-economic factors always influence who is exposed and who can move. While there has been some progress in policy interventions, especially in combating urban air pollution, there has been no systematic attempt to address the environmental health aspects of seasonal migration specifically. Observed migration patterns are not caused by a single factor but by many factors that are determined by socioeconomic vulnerability, which means that integrated research approaches and policies will be needed to address them, and that there will be a need to connect environmental protection, migrant health services, and social equity. The first step in protecting individuals and the environments they traverse is acknowledging the importance of environmental health in the migration of the Chinese population.
This review adds to the literature by providing the first study to connect five environmental health domains of air quality, thermal stress, water and soil pollution, agricultural instability, and destination-site occupational stress to a seasonal calendar for seasonal migration in China, highlighting the times of year when each stressor is most likely to occur, who is most likely to be impacted, and where initiatives can be best coordinated. This framework explicitly considers occupational hazards, seasonality, and the floating population in China; the majority of previous analyses of the global climate-migration nexus have neglected such considerations [, , ].
Statements
Author contributions
WZ conceived the research, designed the systematic review protocol, conducted database searches, screened and selected studies, extracted data, synthesized evidence across five environmental domains, and drafted the manuscript. SA independently screened studies, performed quality assessment, provided critical input on findings, and reviewed the manuscript. Both authors approved the final version and agree to accountability.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The authors declare that they do not have any conflicts of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
1.
National Bureau of Statistics of China. China Statistical Yearbook 2022. Beijing: China Statistics Press (2023).
2.
PengX. China's demographic history and future challenges. Science (2011) 333(6042):581–7. 10.1126/science.1209396
3.
CaiRFengSOppenheimerMPytlikovaM. Climate variability and international migration: the importance of the agricultural linkage. J Environ Econ Manag (2016) 79:135–51. 10.1016/j.jeem.2016.06.005
4.
ChenSOlivaPZhangP. The effect of air pollution on migration: evidence from China. J Dev Econ (2022) 156:102833. 10.1016/j.jdeveco.2022.102833
5.
ChanKWanG. The size distribution and growth pattern of cities in China, 1982-2010: analysis and policy implications. J Asia Pac Econ (2017) 22:136–55. 10.1080/13547860.2016.1266829
6.
BronerFBustosPCarvalhoVM. Sources of comparative advantage in polluting industries. In: Working Paper 18337. Cambridge (MA): National Bureau of Economic Research (2012). 10.3386/w18337
7.
HeringLPoncetS. Environmental policy and exports: evidence from Chinese cities. J Environ Econ Manag (2014) 68(2):296–318. 10.1016/j.jeem.2014.06.005
8.
PereraF. Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: solutions exist. Int J Environ Res Public Health (2018) 15(1):16. 10.3390/ijerph15010016
9.
ZhangQJiangXTongDDavisSJZhaoHGengGet alTransboundary health impacts of transported global air pollution and international trade. Nature (2017) 543(7647):705–9. 10.1038/nature21712
10.
LiZMaZvan der KuijpTJYuanZHuangL. A review of soil heavy metal pollution from mines in China: pollution and health risk assessment. Sci Total Environ (2014) 468-469:843–53. 10.1016/j.scitotenv.2013.08.090
11.
Rodríguez-LadoLSunGBergMZhangQXueHZhengQet alGroundwater arsenic contamination throughout China. Science (2013) 341(6148):866–8. 10.1126/science.1237484
12.
HuangCBarnettAGWangXVaneckovaPFitzGeraldGTongS. Projecting future heat-related mortality under climate change scenarios: a systematic review. Environ Health Perspect (2011) 119(12):1681–90. 10.1289/ehp.1103456
13.
XieLBulkeleyHTozerL A. comparative review of urban climate governance in Chinese and Western contexts. Urban Governance (2021) 1 (2):81–8. 10.1016/j.urgov.2021.11.001
14.
ZhangQZhangHXuH. Health tourism destinations as therapeutic landscapes: understanding the health perceptions of senior seasonal migrants. Soc Sci Med (2021) 279:113951. 10.1016/j.socscimed.2021.113951
15.
Hainan Provincial Bureau of Statistics. Major Indicators on Population. Hainan Statistical Yearbook. Haikou: Hainan Provincial Bureau of Statistics (2019).
16.
ChenQZhouJLiNLiuLLiYLongWet alFactors influencing changes in the quality of life of the Hainan migratory population with hypertension: a survey of the Chengmai Mangrove Bay community. BMC Public Health (2025) 25(1):49. 10.1186/s12889-025-21281-x
17.
Hainan Provincial Bureau of Statistics. Resources and Environment. Hainan Statistical Yearbook. Beijing: China Statistics Press (2019).
18.
Department of Ecology and Environment of Hainan Province. Bulletin of Hainan Province on Environmental Status. Haikou: Department of Ecology and Environment of Hainan Province (2024).
19.
RigaudKKde SherbininAJonesBBergmannJClementVOberKet alGroundswell: Preparing for Internal Climate Migration. Washington (DC): World Bank (2018).
20.
BlackRAdgerWNArnellNWDerconSGeddesAThomasD. The effect of environmental change on human migration. Glob Environ Change (2011) 21(Suppl. 1):S3–11. 10.1016/j.gloenvcha.2011.10.001
21.
LeungCCYuITSChenW. Silicosis. Lancet (2012) 379(9830):2008–18. 10.1016/S0140-6736(12)60235-9
22.
McLemanRAHunterLM. Migration in the context of vulnerability and adaptation to climate change: insights from analogues. Wiley Interdiscip Rev Clim Change (2010) 1(3):450–61. 10.1002/wcc.51
23.
NawrotzkiRJBakhtsiyaravaM. International climate migration: evidence for the climate inhibitor mechanism and the agricultural pathway. Popul Space Place (2017) 23(4):e2033. 10.1002/psp.2033
24.
LimSS. Migration decisions under ambiguity: migration distance effects revisited. Rev Dev Econ (2019) 23(4):1520–39. 10.1111/rode.12623
25.
PageMJMcKenzieJEBossuytPMBoutronIHoffmannTCMulrowCDet alThe PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (2021) 372:n71. 10.1136/bmj.n71
26.
WellsGASheaBO'ConnellDPetersonJWelchVLososMet alThe Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in meta-analyses. Ottawa: Ottawa Hospital Research Institute (2000).
27.
MoolaSMunnZTufanaruCAromatarisESearsKSfetcuRet alChapter 7: systematic reviews of etiology and risk. In: AromatarisEMunnZ, editors. JBI Manual for Evidence Synthesis. Adelaide: JBI (2020). 10.46658/JBIMES-20-08
28.
AromatarisEMunnZ, editors. JBI Manual for Evidence Synthesis. Adelaide: JBI (2020). 10.46658/JBIMES-20-01
29.
GBD 2017 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017. Lancet (2018) 392(10159):1923–94. 10.1016/S0140-6736(18)32225-6
30.
LuXHongJZhangLCooperORSchultzMGXuXet alSevere surface ozone pollution in China: a global perspective. Environ Sci Technol Lett (2018) 5(8):487–94. 10.1021/acs.estlett.8b00366
31.
ChafeZABrauerMKlimontZVan DingenenRMehtaSRaoSet alHousehold cooking with solid fuels contributes to ambient PM2.5 air pollution and the burden of disease. Environ Health Perspect (2014) 122(12):1314–20. 10.1289/ehp.1206340
32.
VignatiEBerkowiczRHertelO. Comparison of air quality in streets of Copenhagen and Milan, in view of the climatological conditions. Sci Total Environ (1996) 189:467–73. 10.1016/0048-9697(96)05247-3
33.
WeiJLiZXueWSunLFanTLiuLet alThe ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environ Int (2021) 146:106290. 10.1016/j.envint.2020.106290
34.
ChenRKanHChenBHuangWBaiZSongGet alAssociation of particulate air pollution with daily mortality: the China air pollution and health effects study. Am J Respir Crit Care Med (2013) 187(1):73–81. 10.1164/rccm.201204-0617OC
35.
ChenZWangJNMaGXZhangYS. China tackles the health effects of air pollution. Lancet (2013) 382(9909):1959–60. 10.1016/S0140-6736(13)62064-4
36.
PopeCADockeryDW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc (2006) 56(6):709–42. 10.1080/10473289.2006.10464485
37.
ZhangJMauzerallDLZhuTLiangSEzzatiMRemaisJV. Environmental health in China: progress towards clean air and safe water. Lancet (2009) 373(9679):1965–77. 10.1016/S0140-6736(09)60461-9
38.
GBD 2013 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries. Lancet (2015) 386(10010):2287–323. 10.1016/S0140-6736(15)00128-2
39.
WongCMLaiHKTsangHThachTQThomasGNLamKBHet alSatellite-based estimates of long-term exposure to fine particles and association with mortality in elderly Hong Kong residents. Environ Health Perspect (2015) 123(11):1167–72. 10.1289/ehp.1408264
40.
ZhangQMengXShiSKanLChenRKanH. Overview of particulate air pollution and human health in China: evidence, challenges, and opportunities. Innovation (2022) 3(6):100312. 10.1016/j.xinn.2022.100312
41.
ZhengZChenLSunNJinYWangY. Pollution, hazards, and health inequalities: a longitudinal exploration of the impact of PM2.5 on depression among rural older adults with different incomes in China. Humanit Soc Sci Commun (2024) 11(1):1–14. 10.1057/s41599-024-04233-5
42.
PaulLABurnettRTKwongJCHystadPvan DonkelaarABaiLet alThe impact of air pollution on the incidence of diabetes and survival among prevalent diabetes cases. Environ Int (2020) 134:105333. 10.1016/j.envint.2019.105333
43.
YangBYMarkevychIBloomMSHeinrichJGuoYMorawskaLet alCommunity greenness, blood pressure, and hypertension in urban dwellers: the 33 communities Chinese health study. Environ Int (2019) 126:727–34. 10.1016/j.envint.2019.02.068
44.
JiangJMaoSXieYChenXAbulaitiKLiuMet alIs residential greenness associated with dyslipidemia and lipid levels in Chinese rural-dwelling adults? The Henan rural cohort study. Environ Sci Pollut Res Int (2022) 29(4):5852–62. 10.1007/s11356-021-16026-3
45.
YangBYLiuKKMarkevychIKnibbsLDBloomMSDharmageSCet alAssociation between residential greenness and metabolic syndrome in Chinese adults. Environ Int (2020) 135:105388. 10.1016/j.envint.2019.105388
46.
NingJZhangYHuHHuWLiLPangYet alAssociation between ambient particulate matter exposure and metabolic syndrome risk: a systematic review and meta-analysis. Sci Total Environ (2021) 782:146855. 10.1016/j.scitotenv.2021.146855
47.
GuoQZhaoYXueTZhangJDuanX. Association of PM2.5 and its chemical compositions with metabolic syndrome: a nationwide study in middle-aged and older Chinese adults. Int J Environ Res Public Health (2022) 19(22):14671. 10.3390/ijerph192214671
48.
CaoBFuKTaoJWangS. GMM-based research on environmental pollution and population migration in Anhui province, China. Ecol Indic (2015) 51:159–64. 10.1016/j.ecolind.2014.09.038
49.
CaoDXuXGuoCLiaoY. The impact of air pollution on the settlement intention of the floating population-empirical evidence from China. Sci Rep (2025) 15(1):13515. 10.1038/s41598-025-96626-y
50.
ChenF. Residential patterns of parents and their married children in contemporary China: a life course approach. Popul Res Policy Rev (2005) 24(2):125–48. 10.1007/s11113-004-6371-9
51.
ChenFChenZ. Air pollution and avoidance behavior: a perspective from the demand for medical insurance. J Clean Prod (2020) 259:120970. 10.1016/j.jclepro.2020.120970
52.
GuHLiuZShenT. Spatial pattern and determinants of migrant workers' interprovincial hukou transfer intention in China: evidence from a national migrant population dynamic monitoring survey in 2016. Popul Space Place (2020) 26(2):e2250. 10.1002/psp.2250
53.
HuangXChenM. Understanding the role of housing in rural migrants' intention to settle in cities: evidence from China. Habitat Int (2022) 128:102650. 10.1016/j.habitatint.2022.102650
54.
HuangXHeDLiuYXieSWangRShiZ. The effects of health on the settlement intention of rural-urban migrants: evidence from eight Chinese cities. Appl Spat Anal Policy (2021) 14(1):31–49. 10.1007/s12061-020-09342-7
55.
LiBHuangJZhangY. The impacts of air pollution on immigrants' settlement intention in China. Environ Sci Pollut Res (2023) 30(16):46587–603. 10.1007/s11356-023-25149-8
56.
LiZYuLGaoFChengHLiuY. Integration failure or integration risk? Revisiting the modality of return migration in China. Appl Spat Anal Policy (2024) 18(1):19. 10.1007/s12061-024-09618-2
57.
LinLZhuY. Types and determinants of migrants' settlement intention in China's new phase of urbanization: a multi-dimensional perspective. Cities (2022) 124:103622. 10.1016/j.cities.2022.103622
58.
LiuTWangJ. Bringing city size in understanding the permanent settlement intention of rural-urban migrants in China. Popul Space Place (2020) 26(4):e2295. 10.1002/psp.2295
59.
LiuYHuangCWuRPanZGuH. The spatial patterns and determinants of internal migration of older adults in China from 1995 to 2015. J Geogr Sci (2022) 32(12):2541–59. 10.1007/s11442-022-2060-z
60.
MaJFuW. Ancestors call me home: cultural distance and settlement intention of domestic migrant workers in China. Cities (2025) 167:106317. 10.1016/j.cities.2025.106317
61.
ShengYYangS. Economic development level and urban settlement intentions of the new-generation migrants in China: direct or interaction effect?China Popul Dev Stud (2020) 3(2):113–41. 10.1007/s42379-019-00031-8
62.
SongYZhuN. Does natural amenity matter on the permanent settlement intention? Evidence from elderly migrants in urban China. Int J Environ Res Public Health (2022) 19(3):1022. 10.3390/ijerph19031022
63.
TieboutCM. A pure theory of local expenditures. J Polit Econ (1956) 64(5):416–24. 10.1086/257839
64.
WangMSunT. Leave for where? The impact of air quality on migration: evidence at the city-pair level in China. Econ Hum Biol (2023) 51:101285. 10.1016/j.ehb.2023.101285
65.
WangCShenJ. Settlement intention of migrants in urban China: the effects of labor-market performance, employment status, and social integration. Appl Geogr (2022) 147:102773. 10.1016/j.apgeog.2022.102773
66.
WangLXuCQiWMaHWangJQiaoJet alSpatial effects and associated factors on migration flows of China from 2005 to 2015. Appl Spat Anal Policy (2023) 16(2):813–30. 10.1007/s12061-022-09501-y
67.
XuFXieYZhouD. Air pollution's impact on the settlement intention of domestic migrants: evidence from China. Environ Impact Assess Rev (2022) 95:106761. 10.1016/j.eiar.2022.106761
68.
XuFYeXXiangS. Investigating the impact of urban greenspace on the settlement intention of domestic migrants: evidence from China. J Urban Plan Dev (2024) 150(4):04024038. 10.1061/JUPDDM.UPENG-4849
69.
YangPZhangXLvWYuX. The impact of innovative cities construction on air pollution: evidence from China. Appl Spat Anal Policy (2025) 18(1):40. 10.1007/s12061-025-09644-8
70.
YuXLiangJZhangY. Air pollution and settlement intention: evidence from the China migrants dynamic survey. Int J Environ Res Public Health (2022) 19(8):4924. 10.3390/ijerph19084924
71.
YueQSongYZhuJLiZZhangM. Exploring the effect of air pollution on settlement intentions from migrants: evidence from China. Environ Impact Assess Rev (2021) 91:106671. 10.1016/j.eiar.2021.106671
72.
YueZLiSFeldmanMWDuH. Floating choices: a generational perspective on intentions of rural-urban migrants in China. Environ Plan A (2010) 42(3):545–62. 10.1068/a42161
73.
ZhangCDuMLiaoLLiW. The effect of air pollution on migrants' permanent settlement intention: evidence from China. J Clean Prod (2022) 373:133832. 10.1016/j.jclepro.2022.133832
74.
ZhaoZLaoXGuHYuHLeiP. How does air pollution affect urban settlement of the floating population in China? New evidence from a push-pull migration analysis. BMC Public Health (2021) 21(1):1696. 10.1186/s12889-021-11711-x
75.
ZhaoZPanJLeiP. Real curve: identifying and quantifying the real environmental effects on migration in China. Ecol Indic (2021) 133:108348. 10.1016/j.ecolind.2021.108348
76.
ZhuY. China's floating population and their settlement intention in the cities: beyond the hukou reform. Habitat Int (2007) 31(1):65–76. 10.1016/j.habitatint.2006.04.002
77.
ZhuYChenW. The settlement intention of China's floating population in the cities: recent changes and multifaceted individual-level determinants. Popul Space Place (2010) 16(4):253–67. 10.1002/psp.544
78.
ZhaoYCaoYTianL. Future projections of heat waves in China using CMIP5 models and estimation of the associated health burden. Int J Climatol (2020) 40(11):4727–40. 10.1002/joc.6490
79.
BasuRSametJM. Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence. Epidemiol Rev (2002) 24(2):190–202. 10.1093/epirev/mxf007
80.
LiCSunYLiaoH. Temporal changes in temperature and heat wave events in China during the period of 1960 to 2017. Atmos Res (2018) 212:80–101. 10.1016/j.atmosres.2018.04.018
81.
GlaserJLemeryJRajagopalanBDiazHFGarcía-TrabaninoRTaduriGet alClimate change and the emergent epidemic of CKD from heat stress in rural communities: the case for heat stress nephropathy. Clin J Am Soc Nephrol (2016) 11(8):1472–83. 10.2215/CJN.13841215
82.
MaWZengWZhouMWangLRutherfordSLinHet alThe short-term effect of heat waves on mortality and its modifiers in China: an analysis from 66 communities. Environ Int (2015) 75:103–9. 10.1016/j.envint.2014.11.004
83.
StrineTWChapmanDPBalluzLSMoriartyDGMokdadAH. The associations between life satisfaction and health-related quality of life, chronic illness, and health behaviors among U.S. community-dwelling adults. J Community Health (2008) 33(1):40–50. 10.1007/s10900-007-9066-4
84.
ZhaoCLiuBPiaoSWangXLobellDBHuangYet alTemperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci USA (2017) 114(35):9326–31. 10.1073/pnas.1701762114
85.
Ministry of Water Resources, People's Republic of China. China Water Resources Bulletin 2015. Beijing: MWR (2016).
86.
Ministry of Ecology and Environment, People's Republic of China. China Ecological Environment Status Bulletin 2020. Beijing: MEE (2021).
87.
Ministry of Environmental Protection and Ministry of Land and Resources, People's Republic of China. National Soil Pollution Survey Bulletin. Beijing: MoEP/MLR (2014).
88.
JärupLÅkessonA. Current status of cadmium as an environmental health problem. Toxicol Appl Pharmacol (2009) 238(3):201–8. 10.1016/j.taap.2009.04.020
89.
HuoXPengLXuXZhengLQiuBQiZet alElevated blood lead levels of children in Guiyu, an electronic waste recycling town in China. Environ Health Perspect (2007) 115(7):1113–7. 10.1289/ehp.9697
90.
WangYZhaoYChenJ. Water security challenges and management in China's Yellow River basin. Water (2020) 12(3):695. 10.3390/w12030695
91.
LiuJYangW. Water sustainability for China and beyond. Science (2012) 337(6095):649–50. 10.1126/science.1219471
92.
LobellDBSchlenkerWCosta-RobertsJ. Climate trends and global crop production since 1980. Science (2011) 333(6042):616–20. 10.1126/science.1204531
93.
WangXChenFDongZ. Dynamics and drivers of land use change in China's desertification-prone regions. J Arid Environ (2012) 75(10):1303–15. 10.1016/j.jaridenv.2012.03.012
94.
XuDTanMH. Land degradation and population migration in Northwest China: the case of Minqin County. Popul Environ (2001) 22(4):413–30. 10.1023/A:1010708914619
95.
ChanKW. The Chinese hukou system at 50. Eurasian Geogr Econ (2009) 50(2):197–221. 10.2747/1539-7216.50.2.197
96.
BernardABellMCharles-EdwardsE. Life-course transitions and the age profile of internal migration. Popul Dev Rev (2014) 40(2):213–39. 10.1111/j.1728-4457.2014.00671.x
97.
BloomDELucaDL. The global demography of aging: facts, explanations, future. In: PiggottJWoodlandA, editors. Handbook of the Economics of Population Aging, 1. Amsterdam: North-Holland (2016). p. 3–56. 10.1016/bs.hespa.2016.06.002
98.
ChenJBaoJ. Chinese 'snowbirds' in tropical Sanya: retirement migration and the production of translocal families. J Ethn Migr Stud (2021) 47(12):2760–77. 10.1080/1369183X.2020.1739377
99.
GuHJieYLaoX. Health service disparity, push-pull effect, and elderly migration in ageing China. Habitat Int (2022) 125:102581. 10.1016/j.habitatint.2022.102581
100.
HoganTDSteinnesDN. Arizona sunbirds and Minnesota snowbirds: two species of the elderly seasonal migrant genus. J Econ Soc Meas (1996) 22(2):129–39. 10.3233/JEM-1996-22203
101.
HuangCLiuYPanZ. Stay, leave late, leave early, return, or move onward? Interprovincial migration decisions of older adults in China, 2000-2005 and 2010-2015. Popul Space Place (2024) 30(8):e2809. 10.1002/psp.2809
102.
HuangCLiuYPanZWuR. Modelling locational choices of older adults in China, 2010-2015. Appl Geogr (2023) 155:102954. 10.1016/j.apgeog.2023.102954
103.
LiQZhouXMaSJiangMLiL. The effect of migration on social capital and depression among older adults in China. Soc Psychiatry Psychiatr Epidemiol (2017) 52(12):1513–22. 10.1007/s00127-017-1439-0
104.
LiuJ. Ageing in rural China: migration and care circulation. J Chin Sociol (2016) 3(1):9. 10.1186/s40711-016-0030-5
105.
WangZXuNWeiWZhaoN. Social inequality among elderly individuals caused by climate change: evidence from the migratory elderly of mainland China. J Environ Manage (2020) 272:111079. 10.1016/j.jenvman.2020.111079
106.
WuRWuL. Migration choices of China's older adults and spatial patterns emerging therefrom (1995-2015). PLoS One (2023) 18(8):e0290570. 10.1371/journal.pone.0290570
107.
XuJMaJ. Urban-rural disparity in the relationship between geographic environment and the health of the elderly. Appl Spat Anal Policy (2024) 17(3):1335–57. 10.1007/s12061-024-09586-7
108.
HuangYGuoF. Welfare programme participation and the wellbeing of non-local rural migrants in metropolitan China: a social exclusion perspective. Soc Indic Res (2017) 132(1):63–85. 10.1007/s11205-016-1329-y
109.
HuangYLiuWZhuoYSunX. Social insurance participation and urban settlement intentions of China's floating population. Popul Space Place (2020) 26(8):e2366. 10.1002/psp.2366
110.
LiuPLongZDingX. The effect of basic public health service equalization on settlement intention of migrant workers in China: the mediating effect model based on subjective feelings. Health Econ Rev (2024) 14(1):58. 10.1186/s13561-024-00534-2
111.
LuNXuSZhangJ. Community social capital, family social capital, and self-rated health among older rural Chinese adults: empirical evidence from rural northeastern China. Int J Environ Res Public Health (2021) 18(11):5516. 10.3390/ijerph18115516
112.
LuHGuoXLiCQianW. Social ties and urban settlement intention of rural-to-urban migrants in China: the mediating role of place attachment and the moderating role of spatial pattern. Cities (2024) 145:104725. 10.1016/j.cities.2023.104725
113.
MacLachlanIGongY. China's new age floating population: talent workers and drifting elders. Cities (2022) 131:103960. 10.1016/j.cities.2022.103960
114.
MerzEMSchuengelCSchulzeHJ. Intergenerational solidarity: an attachment perspective. J Aging Stud (2007) 21(2):175–86. 10.1016/j.jaging.2006.07.001
115.
TangSLeeHFFengJ. Social capital, built environment and mental health: a comparison between the local elderly people and the 'laopiao' in urban China. Ageing Soc (2022) 42(1):179–203. 10.1017/S0144686X2000077X
116.
World Health Organization. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. Geneva: WHO (2021).
Summary
Keywords
agricultural workers, air pollution, China, climate change, environmental health
Citation
Zheng W and Akram S (2026) Environmental health determinants among seasonal migrants in China: a multi-dimensional analysis. Int. J. Public Health 71:1610170. doi: 10.3389/ijph.2026.1610170
Received
14 July 2026
Revised
22 August 2026
Accepted
05 October 2026
Published
22 October 2026
Volume
71 - 2026
Edited by
Ricardo Almendra, University of Coimbra, Portugal
Reviewed by
Ahmed Almohammadi, Ministry of Health, Saudi Arabia
One reviewer who chose to remain anonymous
Updates
Copyright
© 2026 Zheng and Akram.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sara Akram, saraakram452@gmail.com
This Review is part of the IJPH Special Issue “Urban Health in Transition: Advancing Evidence and Policy for Healthier Cities”
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