Impacts of air pollution on farmers' subjective satisfaction in China's ore and agricultural zone.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 3;16:11801. doi: 10.1038/s41598-026-41510-6 Search in PMC Search in PubMed View in NLM Catalog Add to search Impacts of air pollution on farmers’ subjective satisfaction in China’s ore and agricultural zone Marhaba Turhun Marhaba Turhun 1 School of Geography and Tourism, Shaanxi Normal University, Xi’an, 710119 China Find articles by Marhaba Turhun 1 , Xingmin Shi Xingmin Shi 1 School of Geography and Tourism, Shaanxi Normal University, Xi’an, 710119 China Find articles by Xingmin Shi 1, ✉ Author information Article notes Copyright and License information 1 School of Geography and Tourism, Shaanxi Normal University, Xi’an, 710119 China ✉ Corresponding author. Received 2025 Nov 10; Accepted 2026 Feb 20; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13066507  PMID: 41775858 Abstract Understanding the relationship between air pollution and farmers’ subjective satisfaction in ore–agriculture zone is crucial for rural revitalization and improving farmers’ well-being in China. However, existing research lacks multi-perspective and multi-time-frame analyses of this relationship. This study develops a three-dimensional satisfaction framework encompassing life, environmental, and government dimensions and adopts a multi-temporal approach covering short-, medium-, and long-term horizons. Using data from air pollution measurements and questionnaire surveys (n = 600) focusing on farmers in the ore-agriculture zone. An ordinary least squares (OLS) method is applied to assess the effect of the air quality index (AQI) on farmers’ subjective satisfaction across different time frames in the region. The results indicate the following: (1) Air pollution significantly reduces farmers’ subjective satisfaction, encompassing life satisfaction, atmospheric environmental satisfaction, and government satisfaction. (2) The short-term effects of air pollution on farmers’ subjective satisfaction are insignificant or weakly negatively significant, while medium-term and long-term exposure to air pollution results in a significant reduction in subjective satisfaction, with the most pronounced negative impact observed during medium-term exposure. (3) Individual characteristics and socio-economic factors show significant heterogeneous effects on subjective satisfaction. Above results further illustrate the extension of environmental perception theory to rural resource-dependent areas. These findings provide constructing a multi-dimensional and multi-time-frame analytical framework to fill the gap of single-perspective or long-term-focused satisfaction research. The government should regard mid-term air pollution control as a core task of rural revitalization; value farmers’ subjective evaluations of governance work; and strengthen rural infrastructure investments including housing conditions, water quality, and transportation accessibility to mitigate the negative impacts of air pollution. Keywords: Ore-agriculture zone, Air pollution, Life satisfaction, Atmospheric environmental satisfaction, Government satisfaction, Ordinary least squares model Subject terms: Environmental sciences, Environmental social sciences Introduction Coal mining and its utilization, as a key energy source supporting socio-economic progress and daily activities, have consistently garnered significant attention 1 . China is the leading global producer and consumer of coal, with worldwide coal consumption exceeding 8 billion tons for the first time in 2022, reflecting a 1.2% increase from the previous year. Of this, China accounted for 5.41 billion tons of standard coal consumption, a rise of 2.9% from the previous year 2 . Despite its importance, coal mining and transportation have caused numerous environmental issues that require attention 3 , such as land degradation, water scarcity, and declining air quality. Notably, air quality in mining areas is heavily impacted by the significant amounts of dust, slag, and exhaust gases generated during coal mining and transport activities. Deng et al. 4 studied dust on transportation routes, finding that more than 60% of the dust produced consisted of particles between 0 and 10 µm. Wu 5 highlighted that the processes of coal transportation, loading, and stacking generate large volumes of dust, with coal transportation identified as the primary source of air pollution in mining areas. The escalating dust levels in mining regions have led to severe air pollution, posing a serious threat to public health and causing a decline in quality of life and life satisfaction 6 . Subjective satisfaction, as individuals’ comprehensive perception of their living conditions, is the result of the superposition and integration of satisfaction across various domains, and it maintains a close association with air pollution 7 . The impact of air pollution on subjective satisfaction and emotional states operates primarily through two pathways. On the one hand, air pollution directly impairs subjective satisfaction through sensory perceptions. Recent psychological studies have suggested a potential connection between air pollution and psychological distress, such as anxiety 8 and mental disorders 9 . On the other hand, air pollution has a significant impact on physical health, social activity, and quality of life, such as respiratory diseases, cardiovascular problems 10 , and adverse birth outcomes 11 , indirectly lowering human emotional states and subjective satisfaction. Current research on this topic has several limitations. Firstly, the majority of existing studies focus on the relationship between air pollution and individuals’ physical health. For example, Hu and Guo 12 analyzed the severity of health hazards caused by air pollution and the mortality rates attributable to this environmental risk. However, as human needs continue to evolve, people increasingly seek fulfillment at the psychological and emotional levels. The demand for clean air is thus growing not only for health-related reasons but also out of a desire for high-quality life experiences and inner peace. Consequently, scholars have begun to examine the link between air pollution and subjective well-being. For instance, various studies use social survey data 13 , Weibo (Chinese microblog) data 14 , living conditions survey data 15 , and questionnaire-based data 16 to explore how air pollution affects individual happiness (measuring happiness through life satisfaction), life satisfaction, satisfaction with environmental quality, and satisfaction with government services. Secondly, most existing satisfaction-related studies tend to primarily focus on life satisfaction, and few have explored the impact of air pollution on public subjective satisfaction from a broader perspective. Actually, the process of air pollution control provides key insights into the level of local government governance and has become an important measure of public satisfaction with government actions. From the perspective of public policy, feedback from the public indicates their satisfaction with specific areas of governance and plays a crucial role in shaping public policy. Subjective satisfaction evaluation encompasses individuals’ overall assessments of factors such as life events, environmental quality, and the quality of public services provided by the government. Therefore, studying the impact of air pollution on subjective satisfaction from the combined perspectives of life, environment, and public services offers valuable scientific evidence for air pollution control and air quality improvement. Finally, most studies investigating the relationship between air pollution and subjective satisfaction have used datasets based on quarterly or annual data 14 , 17 , with fewer studies using weekly or monthly data, and rarely have studies focused on the daily effects of air pollution on public satisfaction. Subjective satisfaction can be related to an individual’s emotional and psychological state on any given day, making it susceptible to short-term psychological fluctuations 13 . Therefore, it is essential to incorporate air pollution monitoring data that aligns closely with the survey period. The questionnaire process allows for precise recording of the time when respondents answer, facilitating the assessment of air pollution’s impact on subjective satisfaction on the day of the survey. Good rural air quality constitutes a crucial element of China’s ecological environment system, and air governance plays a significant role in the rural revitalization efforts of the country. It also serves as a critical means to improve farmers’ sense of gain and subjective satisfaction. However, existing research predominantly focuses on urban areas, with limited studies addressing the impact of air pollution on the subjective satisfaction of farmers in rural contexts 18 , particularly from comprehensive perspectives such as life satisfaction, atmospheric environmental satisfaction, and governmental satisfaction. The middle reach ore-agriculture zone in the Yellow River Basin represents a distinct area embedded within mining regions that overlap with rural spaces. This zone has contributed over 70% of China’s coal production in the past five years and includes a significant concentration of large and mega coal mines 6 . Nevertheless, coal mining and transportation activities in this area have resulted in a deterioration of air quality, posing various challenges to farmers’ production activities and daily lives. Compared to previous studies, this study offers the following contributions. Firstly, within the context of the largest developing country and focusing on the primary coal mining region in China, the ore-agriculture zone in the middle reaches of the Yellow River Basin, it investigates the impact of air quality index (AQI) exposure on farmers’ life satisfaction, atmospheric environmental satisfaction and government satisfaction across different time frames, including short-term conditions on the day of the questionnaire, medium-term conditions three months prior to the questionnaire and long-term conditions one year prior to the questionnaire. Additionally, the study conducts a differential analysis to examine how individual attributes of farmers and external socio-economic factors influence their subjective satisfaction. Secondly, meteorological factors may affect the diffusion or accumulation of air pollutants. They can also influence farmers’ subjective satisfaction. For example, extreme weather conditions can impair farmers’ emotional state. By incorporating meteorological factors as control variables, the OLS model can eliminate the confounding effects of such factors, thereby enhancing the accuracy and credibility of its estimation results. This study utilizes Stata software to develop the OLS model. Lastly, the empirical findings demonstrate that the different temporal effects of air pollution may reveal deep-seated issues for ore-agriculture zone environmental governance. At the same time, the subjective satisfaction of farmers has information value in improving their quality of life. This study encourages governments around the world to prioritize farmers’ subjective evaluations of development governance, and provides information for the formulation of public policies. The remainder of this paper is structured as follows: Section " Literature review and research hypotheses " presents the literature review and research hypotheses. Section " Research methods and data sources " introduces the study area, research methods, and data sources. Section " Results " reports the main findings and conducts robustness tests. Section " Discussion " discusses the findings, policy implications, and research limitations. Finally, Sect. " Conclusions " concludes the study. Literature review and research hypotheses The theory of environmental perception encompasses individuals’ direct or indirect perception of environmental information 19 . This theory posits that sensation is the sensory response when sensory organs receive environmental stimuli and transmit relevant information to the brain. Perception refers to the process of effectively organizing, processing, and retaining these sensations to form coherent experiences 20 . Air pollution is the most harmful environmental factor. In the sensation stage, air pollutants such as visible dust, particulate matter or pungent gaseous emissions act as external environmental stimuli. These stimuli are primarily captured by individuals’ visual and olfactory sensory organs 21 . The perception stage follows, which is an active cognitive process. The brain organizes, processes, and interprets the raw sensory signals received in the first stage, integrating them with individuals’ personal experiences, prior knowledge, and cognitive frameworks. As a result, residents’ subjective experience of air pollution is generated 22 . The subjective experience of air pollution affects the emotional state and subjective satisfaction of residents. Subjective satisfaction is an evaluative psychological experience rooted in individuals’ needs, cognitive appraisals, and value judgments regarding specific aspects of life or living conditions. It is closely linked to Maslow’s Hierarchy of Needs 23 , as it reflects the subjective feedback on the degree to which needs at all levels are met. The construction of a subjective satisfaction index should encompass the key domains that exert a tangible impact on individuals’ daily lives. The core rationale behind integrating life satisfaction, atmospheric environment satisfaction, and government satisfaction to construct a subjective satisfaction index lies in the fact that these three elements collectively form a holistic closed loop. This loop encompasses the individuals’ micro-level life experiences, the meso-level support provided by the human settlement environment, and the macro-level safeguards offered by public governance. Such an approach not only adheres to the theoretical essence of subjective satisfaction but also ensures its feasibility in empirical research. Subjective satisfaction reflects the individuals’ comprehensive assessment of their own conditions for survival and development, and a singular dimension falls short of fully encapsulating its rich connotations. As early as 1978, Shin and Johnson 24 pioneered the conceptualization of life satisfaction, defining it as a holistic evaluation of one’s quality of life rooted in personally selected criteria. Scholars have subsequently expanded and elaborated on this concept, building on Shin and Johnson’s foundational work. Life satisfaction reflects individuals’ perceptions of their overall quality of life, covering core dimensions such as economic status, health conditions, and social relationships, and serves as a direct manifestation of subjective satisfaction. Atmospheric environmental satisfaction focuses on air environmental quality, particularly the impacts of air pollution on health and living comfort, epitomizing the close nexus between environmental governance and public well-being. In the 1970s, environmental satisfaction begins to emerge as a distinct evaluation dimension focused on ecological quality, separate from general living environment assessments; however, large-scale public surveys on ecological environment satisfaction in China are not systematically conducted until after 2000 25 . The satisfaction of atmospheric environment has gradually become one of the key research directions in environmental satisfaction research. At the beginning of the 2000s, research on government satisfaction is relatively active. Van and Muzzio 26 pointed out that the gap between public expectations and reality led citizens to gradually become disillusioned with the government, thereby altering their levels of satisfaction. Government satisfaction gauges public evaluations of public services, policy implementation, and governance efficiency, acting as a critical metric for assessing government performance and public trust. The dimensions of life satisfaction, atmospheric environmental satisfaction, and government satisfaction constitute a robust framework, avoiding the one-sidedness of single-indicator assessments and offering a more holistic reflection of subjective satisfaction. Centered on the dynamic transmission chain of “air pollution stimuli → sensory perception → cognitive processing → subjective evaluation”, the theory of environmental perception shapes the public’s subjective evaluations of air pollution. Such subjective evaluations directly influence the public’s overall subjective satisfaction, encompassing three core dimensions: life satisfaction, atmospheric environmental satisfaction, and government satisfaction. Air pollution, as a negative environmental stimulus, triggers unpleasant sensory perceptions, which are processed and incorporated into individuals’ overall judgments of life satisfaction 27 . The magnitude of air pollution’s impact on life satisfaction varies geographically, driven by disparities in pollution intensity, regulatory stringency, and public environmental awareness. A considerable number of cross-sectional and panel data studies have confirmed the robust negative impact of air pollution on life satisfaction. For instance, research based on Australia’s Labour Dynamics Survey 28 found that a 10% increase in PM 10 concentrations was associated with a significant decrease in residents’ life satisfaction, leading to an impact comparable to a 1.1–1.9% drop in household income. Relevant studies indicated 29 that PM 2.5 concentrations significantly contributed to the decline in public life satisfaction in China and Japan. Empirical research 30 consistently verified the mediating role of health in the relationship between air pollution and life satisfaction, whereby elevated PM 2.5 and PM 10 concentrations exerted significant adverse impacts on individuals’ life satisfaction. Numerous studies demonstrate that air pollution is the most critical determinant of public atmospheric environmental satisfaction. For instance, surveys in resource-based regions showed that farmers’ dissatisfaction with the atmospheric environment was strongly correlated with dust emissions from coal mining and transportation 31 . Unlike urban residents, rural populations had frequent and prolonged contact with polluted air during farming, herding, and other outdoor activities, leading to more acute perceptions of air pollution 16 . In addition, some scholars also pay attention to the impact of indoor cooking on indoor air environment satisfaction. For instance, Son 32 studies showed that, the concentration of PM 2.5 indoors had a negative impact on the indoor air environment satisfaction. In terms of the relationship between air pollution and government satisfaction, when the public perceive that the government fails to effectively address air pollution, their trust in government institutions declines, translating into lower government satisfaction 33 . Government satisfaction is shaped by the effectiveness of public service delivery. Air pollution control, as a core environmental public service, directly affects public perceptions of government competence. Yao 34 found that due to atmospheric temperature inversion, for every 1 μg/m 3 increase in PM 2.5 concentration, public trust in local governments decreased by 4.1%. Air pollution emerges as a key factor influencing public government satisfaction, particularly in regions with intensive resource development. Moreover, current scholarship primarily focuses on air pollution measured at the annual (long-term) scale, with a notable lack of empirical inquiries into how medium-term (seasonal) and short-term air pollution dynamics correlate with subjective satisfaction. The research hypotheses derived from the above analysis are as follows: H1 Air pollution has a significantly negative impact on the farmers’ life satisfaction . H2 Air pollution has a significantly negative impact on the farmers’ atmospheric environmental satisfaction . H3 Air pollution has a significantly negative impact on the farmers’ government satisfaction . Research methods and data sources Study area The middle reaches of the Yellow River are abundant in mineral resources, particularly coal deposits, making the region a critical energy and chemical base in China 35 , 36 . However, the extensive and prolonged development of these resources leads to numerous ecological and environmental challenges. The ecological environment of the Loess Plateau, which encompasses this region, is inherently fragile 37 , presenting significant barriers to achieving high-quality development within the Yellow River Basin. Frequent natural disasters, heightened human-land conflicts, and polluted atmospheric conditions have severely hindered regional ecological conservation, high-quality economic advancement, and the improvement of human well-being. This area spans primarily across Shaanxi and Shanxi provinces. Shaanxi, rich in mineral resources, ranks among the highest in China for reserves of coal, petroleum, natural gas, and salt 38 , and hosts three major coal bases: Shendong, Shanbei, and Huanglong, which are pivotal to China’s energy development. Similarly, Shanxi, with the largest coal reserves in China, boasts favorable mining conditions and long-established industries, housing significant coal bases in Jinzhong, Jindong, and smaller mines in Jinbei 39 . The ore-agriculture zone, where coal resource development overlaps with agricultural production, faces pronounced issues related to “four mines” (mining, mines, miners, and mining towns) and “three rural areas” (agriculture, rural areas, and farmers). As a crucial area in the Yellow River Basin, the ore-agriculture zone’s sustainable agricultural and rural development is integral to achieving high-quality progress for the middle reaches of the Yellow River. Figure 1 illustrates the selected study area. Fig. 1. Open in a new tab Map showing the study area. Methods and data Subjective satisfaction data The subjective satisfaction data presented in this study were gathered through the “Subjective satisfaction questionnaire for farmers in ore-agriculture zone”, a field questionnaire survey combined with participatory rural appraisal methods conducted between August 15 and September 15, 2023. The sample size of this study is sufficient to represent the target population, with the primary justification lying in the rationality of the spatial distribution characteristics and demographic attributes of the sampled population. To avoid sampling bias, ensure coverage of the heterogeneity inherent in the target population, and guarantee the comprehensiveness of sample spatial distribution in the vicinity of ore-agriculture zone, we adopted a five-stage purposive stratified sampling method. This sampling strategy systematically captured variations across spatial, demographic, and air pollution exposure-related dimensions. In the first step, three prefecture-level cities, Yulin City, Linfen City, and Weinan City, located in Shaanxi and Shanxi provinces, were chosen for their significant coal resources. In the second step, two counties or county-level cities with a higher concentration of coal mines were selected from each of these cities, resulting in a total of six counties or county-level cities. In the third step, one or two townships with coal mines were chosen in each selected county or county-level city, and one or two administrative villages within 2 km of a coal mine were selected, totaling 19 administrative villages. The 2 km buffer zone was determined based on previous studies 40 indicating that this range is directly affected by coal mining, such as dust deposition, gaseous pollutant diffusion, ensuring the sample includes farmers with actual exposure to the key explanatory variable. In the fourth step, to maximize representativeness, questionnaires were distributed in these villages through a combined approach of random encounters and systematic household visits, with approximately 30 interviewees selected from each village. This dual sampling method was designed to address potential biases in single-mode sampling and ensure comprehensive coverage of the target population. A total of 613 questionnaires were distributed, each taking about 25–40 min to complete. The rejection rate was 23%, and after removing invalid questionnaires (e.g., incomplete or incorrect responses), 600 valid questionnaires were collected, resulting in an effective response rate of 97%. The study process involved face-to-face interviews, where the researcher provided explanations for any unclear points and recorded additional important information not covered in the questionnaire. In summary, drawing on nearly five years of field research experience, this survey has enhanced the representativeness of the sample from both spatial and socio-demographic perspectives. Spatially, the sampling design stratifies by coal mining intensity, geographical characteristics, and administrative boundaries, covering both Shaanxi and Shanxi provinces, and avoids over-concentration in any single subregion. Socio-demographically, the sample reflects key population characteristics of the study area. For instance, consistent with local realities, a substantial proportion of young adults engage in out-migration for employment to supplement household income. Overall, the demographic profile of the sample aligns with data from the Seventh National Population Census for the region. The study all protocols have been approved by the Ethics Committee of School of Geography and Tourism Shaanxi Normal University, adhering to the requirements outlined in the Helsinki Declaration. The survey was conducted anonymously, and participants were required to provide informed consent before completing the questionnaire. We confirmed that all methods were performed in accordance with the relevant guidelines and regulations. Subjective satisfaction index Maslow’s Hierarchy of Needs 23 states that a person’s needs include five aspects: physiological needs, safety needs, social needs, esteem needs, and self actualization needs. The hierarchy of needs theory holds that the demand structure of the majority of people in a country is closely related to factors such as its economy, culture, and social conditions 41 . As a country’s overall level of development rises, an increasing proportion of its population have their basic physiological and safety needs met. This, in turn, leads them to place greater emphasis on issues such as environmental quality, government effectiveness and life satisfaction, matters associated with higher levels of Maslow’s hierarchy of needs such as esteem and self-actualization. For the rural population, satisfaction with living conditions, the atmospheric environment and government performance may reflect not only the fulfillment of basic needs but also aspirations for a higher quality of life. The dependent variables in this study include life satisfaction, atmospheric environment satisfaction, and government satisfaction, all of which are measured through the “Subjective satisfaction questionnaire for farmers in ore-agriculture zone” conducted by the research team. To assess satisfaction, the relevant question for life satisfaction was “Overall, how would you evaluate your current living situation? ”; for atmospheric environmental satisfaction, it was “How would you evaluate the living atmospheric environment? ”; and for government satisfaction, it was “How would you evaluate the services provided by government departments in your area of residence?”. Respondents rated these aspects using a Likert five-point scale, with scores ranging from 1 (very dissatisfied) to 5 (very satisfied). In the absence of a specific time series, life satisfaction, atmospheric environmental satisfaction, and government satisfaction are used to gauge the alignment of farmers’ experiences with their expectations and desires regarding life, the environment, and government. The mean scores for life satisfaction, atmospheric environmental satisfaction, and government satisfaction are 3.18, 3.05, and 3.22, respectively, showing a slight skew towards higher satisfaction. This type of positive skew has also been noted in previous research, where positive emotions were more prevalent than negative ones 42 , 43 . Therefore, it is suggested that the observed skew in this survey is consistent with the general pattern of subjective satisfaction. The survey’s timing and location were carefully coordinated during the subjective satisfaction questionnaire process, which allowed for accurate alignment of the air pollution data with the satisfaction responses. Air quality index The main explanatory variable in this study is AQI, a critical indicator of local air pollution in China. AQI is a transformed value that aggregates the concentrations of various pollutants and provides a numerical representation of atmospheric pollution levels. The AQI data used in this study were obtained from Chinese environmental monitoring stations within the research area, offering hourly AQI readings with high quality and continuity, with no missing data. This study analyzed AQI averages over different time frames. Short-term AQI refers to the average value of AQI for the day of the questionnaire, aiming to assess the immediate impact of AQI on farmers’ subjective satisfaction. Additionally, the average AQI for the week leading up to the questionnaire was collected to further explore short-term effects. The medium-term AQI was calculated as the average for the three months prior to the questionnaire, while the long-term AQI corresponds to the annual average leading up to the questionnaire. The short-term AQI reflects the respondents’ direct experience of air pollution on the day of the questionnaire, while the medium- and long-term AQI values capture their broader perceptions of air pollution over a longer timeframe. Figure 2 presents the conceptual model for the research. Fig. 2. Open in a new tab Diagram of the conceptual model. Control variables This study used meteorological data as control variables. Meteorological conditions may influence air pollution, and to isolate the effect of air pollution from weather variables, daily ground meteorological data for the study area in 2023 were gathered. This data included temperature, pressure and wind speed, with sources referencing the National Oceanic and Atmospheric Administration of the United States. Socio-economic variables The socio-economic variables in this study include individual farmers characteristics and socio-economic factors. The individual characteristics of farmers in this study included gender, age, education, income, health status, and labor capacity. Socio-economic factors examined included housing conditions, crop harvesting, water quality condition, public medical services, transportation accessibility, and leisure entertainment. A detailed description of these variables is provided in Table 1 . Table 1. The summary statistics of key variables. Variables Definition N Mean SD Min. Max. y LS Life satisfaction 600 3.182 0.925 1 5 ES Atmospheric environmental satisfaction 600 3.052 1.125 1 5 GS Government satisfaction 600 3.217 0.840 1 5 x AQI day The one day mean of AQI 600 78.15 25.44 44.10 126.51 AQI week The one week mean of AQI 600 84.31 10.64 66.77 108.83 AQI medium-term The 3-month mean of AQI 600 87.68 4.04 79.58 97.46 AQI long-term The 1-year mean of AQI 600 85.60 5.70 76.17 100.96 PM 2.5 day The one day mean of PM 2.5 600 27.13 11.76 15.36 64.87 PM 2.5 week the one week mean of PM 2.5 600 19.83 4.19 14.10 31.56 PM 2.5 medium-term The 3-month mean of PM 2.5 600 22.00 2.11 17.15 27.86 PM 2.5 long-term The 1-year mean of PM 2.5 600 37.64 5.38 27.21 49.97 p Personal characteristics Gender 600 0.593 0.489 0 1 Age 600 62.137 11.397 18 87 Level of education 600 2.313 0.921 1 5 Income 600 2.587 1.085 1 5 Disease status 600 0.875 0.840 0 4 Labor capacity 600 2.225 0.636 1 3 s Socio-economic factors Housing condition 600 3.532 1.047 1 5 Crop harvest 600 2.603 1.042 1 5 Water quality condition 600 2.638 1.354 1 5 Public medical service 600 2.370 0.961 1 5 Transportation accessibility 600 3.855 1.090 1 5 Leisure entertainment 600 1.910 1.043 1 5 Open in a new tab Robustness test Medical and environmental studies showed 27 that air pollutants, especially PM 2.5 , induced changes in the neural structure and neurological function, resulting in damage to the frontal lobe of the brain, which controls stress and emotions and stores memory 44 . Acknowledging PM 2.5 as the primary pollutant with a significant impact on mental health 8 . Compared with other pollutants, PM 2.5 emissions generated by the mining industry have the characteristics of high concentration, wide coverage, and strong persistence, which directly affect the quality of the local public’s living environment 45 . Meanwhile, numerous studies showed 29 , 32 that PM 2.5 was a core factor affecting individual subjective satisfaction. To further assess the influence of air pollution on farmers’ subjective satisfaction, the core explanatory variable is substituted with PM 2.5 instead of AQI. Concentrations of PM 2.5 were also gathered for short-term periods (on the day of the questionnaire and one week prior), medium-term periods (three months prior), and long-term periods (one year prior) to further investigate how PM 2.5 levels affect farmers’ subjective satisfaction. Statistical analysis This study utilized an OLS model in Stata software to analyze the effect of air pollution on farmers’ subjective satisfaction across different time frames (short-term, medium-term, and long-term). The OLS model has been widely applied in research on subjective satisfaction and well-being 46 , 47 . Some studies indicated that the OLS model yielded results comparable to those from ordered models, and its coefficients were easier to interpret than the marginal effects derived from multi-category ordered outcomes 48 . 1 In this study, denotes the k-th type of satisfaction, where k = 1, 2, 3 represents life satisfaction, atmospheric environmental satisfaction, and government satisfaction for the j-th respondent, respectively. the variable represents the air pollution index, which accounts for short-term, medium-term, and long-term exposure to AQI and PM 2.5 . Socio-economic variables include personality characteristics , socio-economic factors . Control variables include local weather conditions . The personality characteristics include gender (coded as 1 for male and 0 for female), education level (1 for illiteracy, 2 for primary school or below, 3 for junior high school, 4 for high school or vocational training, and 5 for college and higher education), household income (1 for under 10,000 China Yuan CNY per year, 2 for 10,000–40,000 CNY, 3 for 40,000–80,000 CNY, 4 for 80,000–120,000 CNY, and 5 for more than 120,000 CNY), illness status (number of ill family members), and labor capacity (1 for no labor ability, 2 for partial labor ability, and 3 for full labor ability). The socio-economic factors include housing conditions (1 for very poor, 2 for poor, 3 for average, 4 for good, and 5 for very good), crop harvesting quality (coded similarly from 1 for very poor to 5 for very good), water quality (coded from 1 for very poor to 5 for very good), public medical services (1 for very poor to 5 for very good), transportation accessibility (1 for very poor to 5 for very good), and leisure entertainment (1 for very poor to 5 for very good). The weather variable consists of temperature, pressure, and wind speed. is the constant value and is the error term. As shown in Table 1 , the sample characteristics are summarized as follows: with respect to gender, females and males account for 40.67% and 59.33% respectively; regarding age, the 50–60 and 60–70 age groups have relatively high proportions, at 25% and 36.83% respectively; in terms of educational attainment, primary and junior high school educated individuals constitute the majority, making up 36.5% and 34.5% respectively; farm households at income level 2 account for the highest proportion, at 48.2%, followed by those at income level 3, which make up 27.3%; for the number of ill family members, 41.2% of households have no sick members, 31.2% and 26.8% have one or two sick members respectively, while households with three or four sick members account for a negligible proportion; as for labor capacity, 11.5% of farm households have no labor capacity, whereas 54.5% and 34.0% have partial and full labor capacity respectively. It can be concluded that the survey respondents are predominantly males and mainly consist of middle-aged and elderly individuals, most of whom have a primary or junior high school education. The gender ratio of the survey sample is close to the data of the Seventh National Population Census. In terms of age, as agricultural incomes remain limited, substantial numbers of young people migrate for employment in other regions, which reduces the proportion of young people among the villages’ permanent inhabitants. In terms of educational attainment, the sample data is close to the data of Shaanxi Province from the Seventh National Population Census, where those with primary and junior high school education account for the largest proportions. Therefore, the sample selected in this study has a certain degree of representativeness. Results Effects of air pollution on life satisfaction In conducting the empirical analysis, the respective variables are first tested for multicol-linearity considering the possible multicollinearity between multiple variables. The results show that the maximum value of the variance inflation factor (VIF) is 6.78 and the mean value is 3.53, indicating that the degree of correlated covariance among the variables is within a reasonable range and that there is no collinearity problem. In this study, farmers’ life satisfaction in the ore-agriculture zone is evaluated based on their overall assessment of living conditions and quality of life, using their own chosen reference standards. To explore the varying impacts of AQI on life satisfaction over different time frames, Tables 2 and 3 present life satisfaction as the dependent variable, with AQI represented by regression results for short-term, medium-term, and long-term periods. In Table 2 , short-term AQI is defined as the AQI concentration on the day of the questionnaire. To ensure the robustness of the findings, AQI concentrations from the week before the questionnaire are also considered, providing further insight into the impact of short-term AQI on life satisfaction. In Table 3 , AQI measures correspond to medium-term (quarterly) and long-term (annual) concentrations. Models in Tables 2 and 3 differ in that Model 1 does not control for meteorological factors, whereas Models 2 and 3 account for these factors. The results show that the regression coefficients for short-term, medium-term, and long-term AQI are all negative. However, regarding short-term AQI (Table 2 ), the AQI value on the questionnaire day in Model 2 does not reach statistical significance, while the AQI value from the week before the questionnaire in Model 3 is significant at the 0.1 level. This suggests that short-term AQI has an insignificant or weakly negatively significant effect on farmers’ life satisfaction. Table 2. Explained variables: life satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The one day mean − 0.004* (0.001) − 0.002 (0.002) − 0.002 (0.003) − 0.001 (0.003) The one week mean − 0.012* (0.005) − 0.001 (0.001) p Gender − 0.110 (0.071) − 0.117 (0.074) − 0.126 (0.074) − 0.112 (0.071) − 0.115 (0.074) − 0.115 (0.074) Age 0.018*** (0.003) 0.018*** (0.003) 0.017*** (0.003) 0.018*** (0.003) 0.018*** (0.003) 0.018*** (0.003) Education 0.120*** (0.038) 0.126*** (0.042) 0.136*** (0.042) 0.117*** (0.038) 0.128*** (0.042) 0.128*** (0.042) Income 0.001*** (0.001) 0.001*** (0.001) 0.001*** (0.001) 0.001*** (0.001) 0.001*** (0.001) 0.001*** (0.001) Disease status − 0.134*** (0.050) − 0.125** (0.050) − 0.122** (0.050) − 0.132*** (0.050) − 0.123** (0.050) − 0.123** (0.050) Labor capacity 0.058 (0.051) 0.071 (0.054) 0.080 (0.053) 0.063 (0.051) 0.071 (0.054) 0.071 (0.054) s Housing condition 0.189*** (0.036) 0.181*** (0.038) 0.172*** (0.038) 0.199*** (0.036) 0.185*** (0.038) 0.185*** (0.037) Crop harvest 0.101*** (0.034) 0.084** (0.034) 0.072** (0.034) 0.086** (0.033) 0.079** (0.034) 0.079** (0.034) Water quality condition 0.023 (0.028) 0.064* (0.033) 0.054 (0.033) 0.044 (0.027) 0.065* (0.033) 0.065* (0.033) Public medical service 0.098*** (0.032) 0.110*** (0.035) 0.106*** (0.035) 0.091*** (0.033) 0.109*** (0.036) 0.109*** (0.035) Transportation accessibility 0.025 (0.033) 0.043 (0.036) 0.042 (0.035) 0.040 (0.033) 0.048 (0.035) 0.048 (0.035) Leisure entertainment 0.039 (0.036) 0.053 (0.038) 0.069 (0.038) 0.028 (0.037) 0.048 (0.038) 0.048 (0.038) meteorological factors No Yes Yes No Yes Yes N 600 600 600 600 600 600 R 2 0.228 0.306 0.312 0.218 0.304 0.305 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Table 3. Explained variables: life satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The 3-month mean − 0.025** (0.010) − 0.043** (0.014) − 0.143*** (0.002) − 0.147*** (0.007) The 1-year mean − 0.041** (0.015) − 0.086*** (0.004) Meteorological factors No Yes Yes Yes Yes Yes N 600 600 600 600 600 600 R 2 0.232 0.315 0.309 0.218 0.302 0.301 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Variables (Personal characteristics and socio-economic factors) and settings are consistent with Table 2 . In Table 3 , both the medium-term and long-term AQI are found to be significant at the 0.05 level, highlighting the strong impact of these factors on life satisfaction. As the medium-term or long-term AQI values increase (indicating more severe air pollution) in the ore-agriculture zone, farmers’ life satisfaction decreases. Furthermore, the regression coefficients for the medium-term model are notably larger than those in the long-term model (Fig. 3 a), indicating that medium-term AQI has a more pronounced effect on life satisfaction compared to long-term AQI. In summary, while short-term AQI does not show significant effects or has only a weak impact on farmers’ life satisfaction in the ore-agriculture zone, both medium-term and long-term AQI demonstrate a significant negative effect. This explains that the research hypothesis H1 holds true. Fig. 3. Open in a new tab Magnitude of medium-term and long-term AQI regression coefficients. Effects of air pollution on atmospheric environmental satisfaction Environmental satisfaction refers to farmers’ overall evaluation of the living environment in their residential area, including aspects such as air quality, green space, noise levels, waste management, and other environmental cleanliness issues, all of which contribute to their satisfaction. Since this study primarily focuses on air pollution issues in ore-agriculture zone, the dependent variable is selected as atmospheric environmental satisfaction. Tables 4 and 5 illustrate the effect of AQI on farmers’ atmospheric environmental satisfaction. Except for the dependent variable, the regression model configurations in these tables were identical to those in Tables 2 and 3 . Table 4. Explained variables: atmospheric environmental satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The one day mean − 0.001 (0.002) − 0.005* (0.002) − 0.003 (0.004) − 0.004 (0.004) The one week mean − 0.016* (0.005) − 0.001* (0.001) p Gender − 0.058 (0.084) − 0.063 (0.086) − 0.073 (0.085) − 0.061 (0.084) − 0.063 (0.086) − 0.052 (0.086) Age 0.003 (0.004) 0.002 (0.004) 0.001 (0.004) 0.003 (0.004) 0.002 (0.004) 0.002 (0.004) Education 0.025 (0.048) − 0.035 (0.051) − 0.019 (0.051) 0.026 (0.048)) − 0.028 (0.051) − 0.033 (0.051) Income 0.001 (0.001) 0.001* (0.001) 0.001* (0.001) 0.001 (0.001) 0.001* (0.001) 0.001* (0.001) Disease status − 0.061 (0.061) − 0.045 (0.062) − 0.051 (0.062) − 0.061 (0.061) − 0.047 (0.062) − 0.048 (0.062) Labor capacity 0.101 (0.065) 0.112* (0.065) 0.124* (0.065) 0.104 (0.065) 0.115* (0.065) 0.112* (0.065) s Housing condition 0.249*** (0.043) 0.254*** (0.045) 0.244*** (0.045) 0.251*** (0.043) 0.260*** (0.045) 0.260*** (0.045) Crop harvest 0.096** (0.040) 0.045 (0.043) 0.025 (0.043) 0.095** (0.040) 0.036 (0.043) 0.038 (0.043) Water quality condition 0.255*** (0.035) 0.166*** (0.041) 0.154*** (0.042) 0.253*** (0.035) 0.170*** (0.041) 0.170*** (0.042) Public medical service 0.038 (0.039) 0.003 (0.042) 0.010 (0.042) 0.034 (0.039) 0.009 (0.042) 0.006 (0.042) Transportation accessibility 0.159*** (0.039) 0.187*** (0.041) 0.190*** (0.041) 0.162*** (0.038) 0.196*** (0.041) 0.200*** (0.041) Leisure entertainment 0.045 (0.044) 0.041 (0.045) 0.059 (0.046) 0.043 (0.044) 0.036 (0.046) 0.030 (0.045) Meteorological factors No Yes Yes No Yes Yes N 600 600 600 600 600 600 R 2 0.276 0.378 0.381 0.277 0.373 0.373 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Table 5. Explanatory variables: atmospheric environmental satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The 3-month mean − 0.025*** (0.011) − 0.080*** (0.014) − 0.138** (0.002) − 0.087** (0.024) The 1-year mean − 0.049** (0.015) − 0.048** (0.014) Meteorological factors No Yes Yes Yes Yes Yes N 600 600 600 600 600 600 R 2 0.282 0.384 0.215 0.277 0.220 0.218 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Variables (Personal characteristics and socio-economic factors) and settings are consistent with Table 4 . The degree of impact of various temporal effects shows that short-term AQI negatively influences farmers’ atmospheric environmental satisfaction at the 0.1 level. Both mid-term and long-term AQI exhibit significant negative effects at the 0.01 level, demonstrating that these timeframes have a more substantial negative impact on farmers’ atmospheric environmental satisfaction (Fig. 3 b). This explains that the research hypothesis H2 holds true. Overall, the influence of AQI on farmers’ atmospheric environmental satisfaction follows the order of medium-term AQI > long-term AQI > short-term AQI across the different time frames. Effect of air pollution on government satisfaction Tables 6 and 7 present the regression results using government satisfaction as the dependent variable, with the regression model settings consistent with those in Tables 2 and 3 , except for the dependent variable. In Table 6 , the AQI concentration on the day of the questionnaire exhibits a significantly negative effect on farmers’ government satisfaction at the 0.1 level, while the AQI concentration during the week preceding the questionnaire shows a negative but statistically insignificant impact on farmers’ government satisfaction. Table 6. Explained variables: government satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The one day mean − 0.007* (0.001) − 0.006* (0.002) − 0.007* (0.003) − 0.006 (0.003) The one week mean − 0.001 (0.005) − 0.001* (0.001) p Gender 0.034 (0.071) 0.019 (0.074) 0.024 (0.073) 0.028 (0.071) 0.018 (0.074) 0.030 (0.074) Age 0.001 (0.004) 0.001 (0.004) 0.001 (0.004) 0.002 (0.004) 0.001 (0.004) 0.001 (0.004) Education 0.050 (0.041) 0.083* (0.047) 0.090* (0.048) 0.046 (0.042) 0.092* (0.048) 0.087* (0.048) Income 0.001 (0.001) 0.001 (0.001) 0.001 (0.001) 0.001 (0.001) 0.001 (0.001) 0.001 (0.001) Disease status − 0.017 (0.047) − 0.020 (0.049) − 0.025 (0.049) − 0.020 (0.048) − 0.022 (0.049) − 0.024 (0.049) Labor capacity 0.076 (0.046) 0.068* (0.049) 0.067* (0.050) 0.072 (0.050) 0.072* (0.050) 0.067* (0.050) s Housing condition 0.072** (0.031) 0.057** (0.031) 0.066** (0.033) 0.088*** (0.032) 0.065** (0.032) 0.066** (0.032) Crop harvest 0.046 (0.035) 0.045 (0.037) 0.031 (0.038) 0.023 (0.035) 0.033 (0.038) 0.034 (0.038) Water quality condition 0.024 (0.028) 0.027 (0.037) 0.032 (0.038) 0.054** (0.027) 0.032 (0.037) 0.032 (0.038) Public medical service 0.019 (0.035) 0.012 (0.039) 0.009 (0.039) 0.034 (0.035) 0.003 (0.038) 0.008 (0.039) Transportation accessibility 0.090*** (0.035) 0.088** (0.034) 0.104*** (0.034) 0.116*** (0.033) 0.100*** (0.034) 0.105*** (0.034) Leisure entertainment 0.009 (0.037) 0.045 (0.040) 0.059 (0.041) 0.023 (0.038) 0.050 (0.040) 0.060 (0.040) Meteorological factors No Yes Yes No Yes Yes N 600 600 600 600 600 600 R 2 0.106 0.191 0.172 0.174 0.177 0.174 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Table 7. Explanatory variables: government satisfaction. Project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 AQI PM 2.5 The 3-month mean − 0.029*** (0.008) − 0.033** (0.012) − 0.038** (0.013) − 0.037** (0.018) The 1-year mean − 0.014** (0.001) − 0.019* (0.010) Meteorological factors No Yes Yes Yes Yes Yes N 600 600 600 600 600 600 R 2 0.019 0.121 0.109 0.067 0.177 0.175 Open in a new tab *, ** and *** refer to 10%, 5% and 1% significance level, respectively. Robust standard errors are in parentheses. Variables (Personal characteristics and socio-economic factors) and settings are consistent with Table 6 . In Table 7 , both medium-term and long-term AQI values demonstrate significantly negative effects on farmers’ government satisfaction at the 0.05 level. The absolute values of the regression coefficients are notably larger in the medium-term model compared to the long-term model (Fig. 3 c), indicating that the medium-term AQI level exerts a substantially greater negative impact on farmers’ government satisfaction. Therefore, H3 is established. Robustness test results To assess whether air pollution across different timeframes in the ore-agriculture zone impacts farmers’ subjective satisfaction, robustness tests are conducted by replacing AQI with PM 2.5 . The effects of short-term, medium-term, and long-term PM 2.5 exposure are analyzed with respect to farmers’ life satisfaction, atmospheric environmental satisfaction, and government satisfaction. Regarding life satisfaction (Tables 2 and 3 ), PM 2.5 levels on the day of the questionnaire and during the week prior to the survey have a negative but statistically insignificant effects. However, both medium-term and long-term PM 2.5 exposure have significant negative impacts at the 0.01 level, with the effects of medium-term exposure exceeding those of long-term exposure. For atmospheric environmental satisfaction (Tables 4 and 5 ), PM 2.5 levels on the questionnaire day exert no significant impact, whereas those in the preceding week exhibit a weakly significant negative effect at the 0.1 level. Both medium-term and long-term PM 2.5 exposure exert significant negative effects on atmospheric environmental satisfaction at the 0.05 level, with medium-term exposure having a more pronounced effect than long-term exposure. Regarding government satisfaction (Tables 6 and 7 ), short-term PM 2.5 exposure exhibits no significant or only weakly significant effects. Medium-term PM 2.5 exposure negatively impacts government satisfaction at the 0.05 level, while long-term PM 2.5 exposure has a significant negative impact at the 0.1 level. The impact hierarchy for government satisfaction is mid-term PM 2.5 > long-term PM 2.5 > short-term PM 2.5 . Overall, the analysis confirms that medium- and long-term air pollution in the ore-agriculture zone has significant adverse effects on farmers’ subjective satisfaction, with medium-term exposure exerting a greater influence than long-term exposure in most contexts. Based on the research findings and robustness test results, this study explains the underlying mechanisms of the empirical results. The formation of the three dimensions of satisfaction, namely life satisfaction, satisfaction with the atmospheric environment, and satisfaction with government performance, follows the dynamic chain of “air pollution stimuli → sensory perception → cognitive processing → subjective evaluation” as proposed in the theory of environmental perception. This study find that the impact of short-term air pollution on the three dimensions of satisfaction is either weak or statistically insignificant. This finding is relatively consistent with the conclusion drawn from studies on the impact of air pollution on physical health. For example, Hu 49 found that short-term air pollution (using AQI data from the week before the survey) had no significant effect on frailty, a composite health indicator among older adults; the study further noted that short-term fluctuations in air pollution were unlikely to alter an already established chronic health profile. From the perspective of the relationship between short-term air pollution and farmers’ subjective satisfaction, short-term air pollution manifests as occasional and temporary disruptions rather than permanent characteristics of the medium-to long-term living environment or governance measures. Farmers believe that short-term exposure to air pollution does not exert a significant impact on their health. Accordingly, such exposure is not included in the core assessment of their quality of life, is not regarded as a sign of environmental deterioration, and is less attributed to inadequacies in government governance. This study verifies that mid-term (seasonal) air pollution in ore-agriculture zone has the strongest negative impact on farmers’ life satisfaction, atmospheric environmental satisfaction, and government satisfaction. Relevant studies have shown that compared to other time periods, mid-term air pollution is more strongly and statistically significantly associated with cardiorespiratory mortality 50 . Mid-term air pollution elevates farmers’ perception of air pollution from accidental occurrences to a current norm. In terms of life satisfaction, the health discomfort (e.g., respiratory problems) and production losses (e.g., reduced labor capacity caused by health discomfort) directly lower the quality of life. In terms of satisfaction with the atmospheric environment, such persistent pollution constitutes a direct reflection of deteriorating air quality. In terms of satisfaction with government performance, persistent air pollution signals inadequate government governance, resulting in a shift in farmers’ attitudes toward governance from expectation to disappointment. The cognitive processing of the three dimensions of satisfaction is synchronized, and all reach the threshold for negative evaluation of air pollution. Under long-term (annual) air pollution, farmers’ sensory sensitivity to air pollution gradually diminishes, and they can further adapt to polluted conditions through behavioral adjustments. This adaptive process not only mitigates the progressive deterioration in quality of life but also reduces the intensity of negative evaluations regarding the atmospheric environment. Over the long term, farmers come to perceive insufficient government efforts in air pollution control as the new normal rather than a dereliction of environmental responsibilities; this perception alleviates the adverse impacts on all three dimensions of satisfaction. As a result, the long-term negative impact of air pollution is weaker than that of mid-term air pollution. The above analysis further underscores the temporal effect of air pollution on subjective satisfaction. This study further demonstrates that mid-term air pollution assessment is more policy practical than short-term and long-term assessments. The influence of personal attributes and socio-economic factors on subjective satisfaction This study reveals individual differences in the characteristics of farm households in relation to subjective satisfaction. In terms of life satisfaction, factors such as age, education, and income are found to have a positive and significant impact. Conversely, the number of sick individuals in the household negatively influences life satisfaction. With respect to atmospheric environmental satisfaction, both income and labor capacity are positively and significantly associated with higher levels of satisfaction. Regarding government satisfaction, education and labor ability are also positively and significantly related to satisfaction with the government. This study emphasizes the role of socio-economic conditions as significant factors affecting the subjective satisfaction of farmers. In terms of life satisfaction, factors such as housing conditions, crop harvesting, water quality, and public medical services are found to have positive and significant effects. For atmospheric environmental satisfaction, housing conditions, transportation accessibility, and water quality conditions are positively and significantly associated with higher satisfaction. Regarding government satisfaction, both housing conditions and transportation accessibility have a positive and significant influence on farmers’ satisfaction with the government. Discussion Different temporal effect Previous research has largely concentrated on investigating the effect of regional air pollution on subjective well-being 51 . However, studies examining the overall impact of air pollution on life satisfaction, atmospheric environmental satisfaction, and government satisfaction, particularly in the context of farmers, remain scarce. This study evaluated farmers’ subjective assessments of air pollution changes from multiple perspectives, with empirical results grounded in more precise data matching, thus enhancing the reliability of the findings. Some scholars have distinguished between short-term and long-term life satisfaction, exploring how air pollution affects both 52 . However, this study further confirms that the different temporal effects of air pollution exert different and significant negative impacts on farmers’ subjective satisfaction. As illustrated in Fig. 4 , this figure delineates the mechanism underlying the influence of air pollution on subjective satisfaction. The study revealed that with prolonged exposure, the relationship between air pollution and subjective satisfaction became more evident and intensified, especially when the average air pollution level was assessed over a specific time period, such as a quarterly (three-month) duration. These findings suggest that the negative impact of air pollution on subjective satisfaction is time-dependent, and the cumulative effect of exposure may shape the perception of air pollution. This study providing targeted policy guidance for rural environmental governance. It clearly identifies the control of mid-term persistent air pollution as a core task of rural revitalization, with policy design shifting from blanket governance to targeted interventions during critical periods. This involves establishing a quarterly pollution control mechanism to specifically address key mid-term pollution sources in mining-agricultural composite areas, such as coal transportation and mining activities. Fig. 4. Open in a new tab Mechanism of air pollution’s impact on subjective satisfaction. Analysis of individual variability and socio-economic factors in subjective satisfaction This study identified notable individual variations in the effects of factors such as “age, education, income, disease status” “income, labor ability” and “education, labor ability” on life satisfaction, atmospheric environmental satisfaction, and government satisfaction, respectively. For example, older individuals typically report higher life satisfaction, consistent with findings from other regions 53 . Younger and middle-aged farmers experience greater social pressures, and as they age, their expectations tend to decrease, making older farmers more likely to express satisfaction and thus leading to higher life satisfaction. Farmers with higher educational attainment generally have better physical and mental health, which contributes to their higher life satisfaction. Income is also positively associated with life satisfaction, aligning with previous studies 54 . Enhancing farmers’ income plays a key role in improving their life satisfaction. This study shows that although income has a significant positive impact on life satisfaction at the 1% level, indicating strong significance. The regression coefficient of the income variable is 0.001. This indicates that income variables have a strong correlation with life satisfaction, but in terms of actual impact, the contribution of income to farmers’ life satisfaction is relatively weak. This may be due to the fragile ecological environment and prominent air pollution in the ore-agriculture zone, where income has a strong and significant impact on life satisfaction. However, due to geographical location and other individual and socio-economic factors as the dominant influencing factors, the results show that the contribution of income variables is relatively weak. Multiple studies 55 found that the health status of family members significantly affected life satisfaction, with better health leading to greater life satisfaction. This study suggested that an increased number of sick individuals in the household resulted in lower life satisfaction. Regarding atmospheric environmental satisfaction, income had a positive and significant influence, a finding consistent with earlier studies 15 . Although income has a positive significance at the 5% level of household atmospheric environmental satisfaction, but the regression coefficient is 0.001. This indicates that the positive correlation between income and atmospheric environmental satisfaction has statistical reliability, while clearly reflecting weak contribution characteristics. In addition, greater labor capacity increases both economic returns and the quality of the living environment, leading to higher atmospheric environmental satisfaction. From the perspective of government satisfaction, education level is positively related to government satisfaction. Higher educational attainment improves farmers’ overall knowledge and understanding, resulting in a more rational evaluation of government policies, thereby increasing government satisfaction. Similarly, greater labor capacity among farmers is positively associated with government satisfaction. Enhanced labor capacity, which is reflected in stronger physical health, technical skills, and adaptability, enables farmers to engage more effectively in community affairs and public decision-making processes. This increased participation fosters a stronger sense of agency and inclusion, which in turn improves their perception of government responsiveness and fairness. Moreover, farmers with higher labor capacity are better able to understand and access public services and policy information, leading to more informed and favorable evaluations of government performance. As a result, their trust in and satisfaction with the government tend to increase. This study highlighted that “housing conditions, crop harvesting, water quality, and public medical services,” “housing conditions, transportation accessibility, and water quality,” and “housing conditions, transportation accessibility” were the main socio-economic factors positively influencing farmers’ life satisfaction, atmospheric environmental satisfaction, and government satisfaction, respectively. Previous studies 56 showed that residential satisfaction declines when housing conditions fall short of individuals’ expectations. A durable home that can withstand natural disasters helps ensure farmers’ safety, thus boosting life satisfaction. Crop harvesting also positively impacts life satisfaction, as a good harvest not only provides material security but also improves farmers’ well-being and living standards through increased income. Gao 57 noted that mining activities affected both public living conditions and water quality, which in turn influenced life satisfaction. Access to clean and safe drinking water significantly improves life satisfaction 58 . Public medical services are also an important factor in enhancing farmers’ life satisfaction, improving both physical and mental health while reducing healthcare costs. These services are crucial in mitigating some of the negative effects of local air pollution. Regarding atmospheric environmental satisfaction, this study found that a favorable housing environment, such as good air quality and a green living space, improves atmospheric environmental satisfaction. Notably, this study finds that traffic accessibility is significantly positively associated with farmers’ satisfaction with the atmospheric environment. Although this appears to contradict the conventional view that transportation contributes to pollution, it is reasonable in the context of ore-agriculture zone. On one hand, local air pollution primarily stems from mine dust and agricultural activities, with motor vehicle emissions play a limited role. On the other hand, improved traffic accessibility is often accompanied by infrastructure upgrades, such as paved roads and water-sprinkling dust suppression, which effectively reduce airborne dust and enhance atmospheric visibility. Since visibility serves as the most intuitive cue for farmers in assessing air quality condition, it substantially strengthens their positive evaluation of the atmospheric environment. This study further verifies the positive impact of water quality on atmospheric environmental satisfaction. Water pollution and air pollution often stem from common production activities, and environmental remediation measures have synergistic effects. In ore-agriculture zone, water pollution and air pollution typically originate from shared human activities (such as mining). Better water quality indicates more effective pollution control measures in mining areas, including sealing of transport vehicles, road sprinkling for dust suppression, and wastewater treatment. These governance measures simultaneously reduce the emission of air pollutants, directly improving air quality. In terms of government satisfaction, efforts to improve the housing conditions of rural farmers aim to meet their growing aspirations for a better quality of life 59 . Enhancing the quality of agricultural housing construction improves living conditions and boosts satisfaction with the government. Investments in rural infrastructure, such as road and public transportation improvements, address farmers’ travel challenges. Housing conditions and transportation accessibility are fundamental socio-economic factors that shape farmers’ government satisfaction by fostering trust and support for the government, thereby increasing overall satisfaction. Policy implications and research limitations This study offers several policy implications. First, improving air quality is a vital aspect of China’s rural revitalization and significantly contributes to enhancing farmers’ sense of well-being and quality of life. Mid-term air pollution control should be recognized as a core task of rural revitalization, with policy formulation shifting from comprehensive governance to targeted governance during critical periods. Local governments at all levels should prioritize air quality improvement as a central development focus to enhance both life satisfaction and atmospheric environmental satisfaction among farmers in the ore-agriculture zone. Second, farmers’ government satisfaction provides valuable insights, and objective improvements in government performance are likely to receive favorable evaluations from farmers. Therefore, the government should give due importance to farmers’ subjective assessments of governance while implementing policies to improve air quality. Third, key socio-economic factors such as housing conditions, water quality, and transportation accessibility have a significant impact on farmers’ subjective satisfaction. It is essential to foster greater cooperation between government departments to strengthen the management of rural housing construction, water resource protection, and the development of the rural public transportation infrastructure in the ore-agriculture zone. This study investigates the impact of air pollution on farmers’ subjective satisfaction across different time frames and analyzes the factors influencing this relationship in the ore-agriculture zone in the middle reaches of the Yellow River Basin in China. However, it is important to highlight the study’s limitations and suggest avenues for future research. First, the AQI (PM 2.5 ) concentration in the ore-agricultural composite zone may also affect neighboring rural areas. While most existing research focuses on urban air pollution, effective prevention and control of rural air pollution is one of the key tasks for comprehensive ecological environment protection. Future studies could examine how air pollution from ore-agriculture zone in other regions or countries influences nearby rural or urban populations. Second, this study uses daily AQI (PM 2.5 ) concentration as a measure of short-term exposure. Future research could explore whether air pollution exposure over even shorter periods impacts subjective satisfaction. Finally, while this study focuses on the effects of air pollution on subjective satisfaction across different time frames, future research could use longitudinal data to track the dynamic changes in these variables over time and investigate whether subjective satisfaction improves significantly, along with other related factors. Conclusions This study fills the current research gap by examining the relationship between air pollution and farmers’ subjective satisfaction from different perspectives and time frames. The main objective of this study is to explore and determine the time frames of air pollution exposure, that exert a significant negative impact on the life satisfaction, atmospheric environmental satisfaction, and government satisfaction of farmers in the ore-agriculture zone. Air pollution has a significant negative impact on farmers’ life satisfaction, emphasizing the detrimental effect of air pollution on their healthy life. Air pollution leads to a substantial decrease in farmers’ atmospheric environmental satisfaction, implying that their atmospheric environmental satisfaction may reflect actual air quality conditions. Air pollution significantly lowers farmers’ government satisfaction, indicating that the effectiveness of air pollution control influences their evaluation of government performance. While the short-term effects of air pollution on farmers’ subjective satisfaction are not significant or weakly negatively significant, medium- and long-term exposure to air pollution substantially reduces farmers’ satisfaction, with medium-term exposure showing a particularly strong negative effect. Significant individual differences emerge in farmers’ satisfaction levels: life satisfaction varies by age, education, income, and health status; atmospheric environmental satisfaction differs according to income and labor capacity; and government satisfaction is associated with education and labor capacity. Key socio-economic factors that positively influence farmers’ satisfaction include “housing conditions, crop harvest, water quality, and public medical services” for life satisfaction, “housing conditions, transportation accessibility, and water quality” for atmospheric environmental satisfaction, and “housing conditions and transportation accessibility” for government satisfaction. Farmers’ subjective satisfaction has information value, and improvements in government governance are likely to be positively reflected in farmers’ evaluations. Therefore, the government should prioritize the subjective evaluation of farmers on development governance and implement policies aimed at improving air quality to enhance the quality of life for farmers in the ore-agriculture zone. Acknowledgements We extend our gratitude to Li Xueping, Jiang Biyao, Wang Fei, Zhu Rongfang, An Ran, Wen Shaocong and others from the School of Geography and Tourism at Shaanxi Normal University for their participation in the questionnaire survey and data entry. Thank you for your invaluable contributions to this research. Author contributions Marhaba Turhun: Conceptualization, Investigation, Methodology, Formal analysis, Data curation, Writing—Original Draft, Visualization, Software. Xingmin Shi: Conceptualization, Validation, Writing—Review& Editing, Supervision, Project Administration, Funding acquisition, Resources. Funding Humanities and Social Science Fund of Ministry of Education of the People’s Republic of China (21YJA840014). Shaanxi Province Key Research and Development Program Project (2021ZDLSF05-02). Natural Science Research Project of Shaanxi Province (2025JC-YBMS-279). Data availability The datasets used during the current study available from the corresponding author on reasonable request. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Yang, Q. X., Zhao, B. Q. & Guo, D. G. A review on vegetation restoration of opencast coal mine areas in northern China. Chin. J. Ecol. 34 (4), 1152–1157 (2015). [ Google Scholar ] 2. Mao, J. W., Liu, M., Yao, F. J., Xie, G. Q. & Yuan, S. D. Some points concerned in field of prospecting and exploration in China and future considerations. Miner. Depos. 6 (2), 1–12 (2024). [ Google Scholar ] 3. Wang, Y. X. et al. 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