Tuberculosis is a chronic infectious disease with various manifestations and gradual course, and it is still the main health problem and one of the leading causes of death in the world. Due to the high contagious rate, preventive measures against this disease significantly reduces human mortality. Since the discovery of this disease, it has always been the focus of scientists and researchers in various scientific disciplines. To eradicate this disease, many studies and research have been done that rely on recognizing the effective factors and ways to deal with it. In this study, spatial factors refer to geographic and environmental variables with a mappable spatial distribution (e.g., temperature, dust), while non-spatial factors refer to individual-level clinical or demographic data (e.g., age, HIV status). Studies have shown that temperatures, rainfall, wind speed, and air pressure affect tuberculosis (Cao et al., 2016). Climate change is another important and influential factor in causing respiratory and infectious diseases that have been less considered so far. Recent studies have shown that climate change has caused many changes in the distribution and spread of tuberculosis. These changes cause mutations in the disease, and increase the disease's resistance to existing drugs; as a result, drug-resistant tuberculosis has developed (Anigbo and Choudhary, 2018).

Moreover, recent research highlights how climate change can increase vulnerability to tuberculosis by affecting food security, nutritional status, water quality, and healthcare access, thereby exacerbating both transmission rates and disease severity (Sinha et al., 2021). Additionally, researchers found that exposure to environmental air pollutants, including occupational and indoor exposure to dust and smoke, significantly increases the risk of tuberculosis infection, suggesting the need to consider air quality as a crucial factor in tuberculosis control strategies (Davis and Checkley, 2021). Furthermore, environmental factors such as ventilation area, occupancy density, lighting intensity, humidity, and room temperature have been demonstrated to significantly influence the incidence of pulmonary tuberculosis, highlighting the critical importance of the quality of the indoor environment in tuberculosis prevention efforts (Nurany et al., 2022). Recent studies in Zhejiang Province, China, also found significant associations between meteorological factors and tuberculosis, revealing that low temperature and low relative humidity significantly increase tuberculosis risk, with GDP per person, population density, and latitude modifying these effects (Wu et al., 2024). Another study conducted in Changde City, Hunan Province, showed a positive correlation between tuberculosis incidence and meteorological variables such as mean air temperature, sunshine hours, and pollutants like PM2.5, PM10, and O3, indicating that these factors have a significant lagged and nonlinear impact on tuberculosis transmission (Sun et al., 2025). Recent studies also identified nonlinear lagged effects of temperature and humidity on tuberculosis notifications, demonstrating significantly increased risks associated with specific temperature and humidity ranges over extended lag periods (Xu et al., 2021a). Recent findings also indicate that tuberculosis complex mycobacteria (MTBC) survive long-term in the soil, maintaining their pathogenicity and virulence, which significantly contributes to environmental transmission risks (Ghodbane et al., 2014). Further, advanced machine learning models have successfully demonstrated the predictive relationship between tuberculosis incidence and meteorological factors such as average temperature, sunshine duration, PM10, and air pollutants, highlighting the importance of integrating these environmental factors into tuberculosis control and prevention strategies (Tang et al., 2023). Additionally, research underscores that MTBC contamination in environmental mediums like soil, water, pasture, air, and dust significantly facilitates tuberculosis transmission, emphasizing a multidisciplinary 'One Health' approach to control and manage environmental risks. This study highlighted young adults and relative humidity as significant factors increasing tuberculosis transmission risk in healthcare settings, suggesting that these elements should be key considerations in infection control interventions (Zhang et al., 2022).

Additional research indicates that environmental sanitation factors, particularly inadequate sanitation and poor personal hygiene practices, significantly increase the risk of tuberculosis infection, emphasizing the critical role of proper sanitation and hygiene in controlling the spread of tuberculosis (Firjoun Ali et al., 2024). Another research confirms that physical environmental factors such as inadequate ventilation, high occupancy density, insufficient natural lighting, and high humidity significantly contribute to the incidence of tuberculosis (Satwikasari, 2018). Moreover, researchers found significant concentrations of tuberculosis in environmental samples such as soil and water, suggesting potential environmental reservoirs for tuberculosis and highlighting the importance of environmental management in controlling disease spread (Velayati et al., 2015). A recent analysis also demonstrated the clear spatial aggregation of tuberculosis cases and identified meteorological factors like temperature, humidity, precipitation, and air pollutants (PM10, NO2, SO2, O3, PM2.5) as significantly influencing the spatial and temporal distribution of tuberculosis, suggesting targeted interventions in hotspot regions (Li et al., 2022). Previous research done on environmental and climatic conditions has shown that tuberculosis also occurs seasonally. According to statistics obtained in several different countries, the peak incidence of tuberculosis is in spring and summer, consequently, it can be concluded that winter is the infection time of this disease (Fares, 2011). Therefore, it can be said that climate and environment have a significant impact on the prevalence of this disease.

Although numerous studies have explored the relationship between environmental factors and tuberculosis, there is limited research specifically investigating the combined impact of spatial and non-spatial factors using geospatial information systems (GIS) and fuzzy logic approaches, especially in regions with high tuberculosis incidence like Sistan and Baluchestan Province, Iran. Similar methodologies have been applied in other regions such as China and Ethiopia. For example, Cao et al. used Bayesian spatial-temporal analysis to map tuberculosis in China (Cao et al., 2016), while Gelaw et al. assessed environmental factors like temperature and altitude using GIS approaches in Ethiopia (Gelaw et al., 2019). However, these studies typically examined spatial or climatic effects in isolation, lacking integrated fuzzy logic frameworks to model uncertainty across diverse data types as done in our study.

According to the reports of the World Health Organization (WHO), about 10 million people are infected in the world with tuberculosis annually (Quaife et al., 2020), between 8000 and 80,000 people of whom live in Iran (Yazdani Charati et al., 2019). Among the provinces of Iran, Sistan and Baluchestan province has the highest rate of tuberculosis (Marvi et al., 2017). While several methods such as Bayesian hierarchical models and geospatial regression have been used to analyze tuberculosis patterns, these approaches often assume strict statistical distributions or require extensive data normalization. Fuzzy logic, in contrast, accommodates imprecision in real-world environmental and health data, enabling effective integration of heterogeneous spatial and non-spatial inputs. This makes it particularly suited for complex, multi-factorial diseases like tuberculosis in under-resourced settings. Therefore, this article examines the impact of environmental factors and climate change in Sistan and Baluchestan province using GIS and Fuzzy Logic.

The contribution of this study is structured into three main steps: first, identifying key spatial and non-spatial factors affecting tuberculosis incidence through literature review and statistical analysis; second, applying fuzzy logic rules separately for spatial and non-spatial data sets; and third, evaluating and mapping the combined and individual impacts of these factors using GIS to identify areas of high tuberculosis risk for targeted public health interventions. The spatial analysis was conducted at the sub-provincial level, incorporating both district-level and localized environmental data layers such as temperature, dust, and proximity to water sources. This multi-scale approach enhances the granularity of tuberculosis risk mapping. Recognizing these factors not only informs risk assessment but also guides public health interventions. For instance, ensuring proper ventilation in high-density living spaces, enforcing air pollution controls, and optimizing housing standards in high-risk areas can substantially reduce transmission. By identifying these factors, their impact can be reduced or eliminated if possible; as a result, tuberculosis can be prevented.