4.1. Land Use/Land Cover Change
Our study divides the Coimbatore district into several land use/land cover (LULC) classes, namely: built areas, agricultural land, barren land, water bodies and forest. The visual representation in Fig. 6 illustrates changes in the LULC distribution over the past three decades. The area was characterised primarily by forests and agricultural land. However, the trend indicates a shift from a largely rural landscape toward urbanisation. The percentage of changes for each category is detailed in Table 1. We visually interpreted the change detection results for better comprehension, as depicted in Fig. 6, which showcases the LULC maps of the study area for 1985, 2000, 2009, and 2015.
Built-up Area
This category includes areas that exhibit intensive use, largely covered by human-made structures. Our study identified an expansion in the built-up area from 8,093.3 hectares in 1985 to 27,593.8 hectares in 2015. A significant conversion of agricultural land into a built-up area is observed (Fig. 6), an outcome likely due to population growth, industrialisation, and the expansion of the boundaries of the Coimbatore Corporation in the northern and southern parts of the district.
Agricultural Land
Agricultural land serves primarily for agriculture, producing food, fibre, and other commercial and horticultural crops, covering irrigated, unirrigated, and plantations. During the 30-year period, agricultural land decreased from 46,293.6 hectares in 1985 to 20,353.8 hectares in 2015, with more than half of agricultural land transitioning to other uses, as evidenced in Fig. 6.
Barren Land
Barren land saw an increase from 29,243.9 hectares in 1985 to 43,724.9 hectares in 2015. This change is attributed to changes in agricultural practices and increased urbanization (Pandian et al., 2014).
Forests
The western part of the study area, which is covered by the western ghats, demonstrates a declining trend in forest land. The expansion of agricultural land was observed to occur predominantly at the expense of forest land between 1985 and 2015, since forest coverage decreased from 36,525.4 hectares in 1985 to 34,290.4 hectares in 2015.
Water Bodies
The Coimbatore district has several lakes. Waterbody coverage in the study area ranged from 123.32 hectares in 1985, peaking at 446.78 hectares in 2009, and reducing to 205.76 hectares in 2015. The water resources were used primarily for built areas, industries and transportation purposes.
Table 1
Yearly changes in land use/land cover (ha)
LULC
|
Area-1985 (ha)
|
Area-2000 (ha)
|
Area-2009 (ha)
|
Area-2015 (ha)
|
Agriculture
|
46293.6
|
45406.8
|
34588.3
|
20353.8
|
Barren land
|
29243.9
|
29130.2
|
30673.3
|
43724.9
|
Built up area
|
8093.2
|
13489.1
|
20938.1
|
27593.8
|
Coconut/Homestead vegetation
|
13909.9
|
10312.9
|
12180.8
|
8020.0
|
Forests
|
36525.4
|
35546.4
|
35362.1
|
34290.4
|
Water bodies
|
123.3
|
303.2
|
446.8
|
205.8
|
In this study, we performed an accuracy assessment for each of the four years under consideration: 1985, 2000, 2009, and 2015. Table 2 displays the overall accuracy of the multi-temporal LULC maps. Overall, for all years, the LULC classification products had achieved significantly higher classification accuracies, as depicted by their overall accuracies and the kappa coefficients (Table 2).
Table 2
Accuracy Assessment for Land Use /Land Cover Change
Acquisition Year
|
Total Samples
|
Overall Accuracy
|
Kappa Coefficient
|
1985
|
130
|
78.89%
|
0.754
|
2000
|
120
|
82.89%
|
0.824
|
2009
|
135
|
83.89%
|
0.814
|
2015
|
125
|
88.89%
|
0.854
|
We observed that during the past decade, from 2005 to 2015, the population of Coimbatore has seen an increasing trend, leading to a corresponding increase in the built-up area to meet the demands of economic development and infrastructure needs. A correlation analysis was performed between population growth and the expansion of the built-up area. The results indicate a strong positive correlation, with a correlation coefficient value of 0.96, between the two variables. Furthermore, the p-value of 0.019 suggests a statistically significant relationship between population growth and the expansion of the built-up area. As depicted in Fig. 7, the correlation demonstrates that an increase in population invariably leads to an expansion of the built-up area in the study area.
Our results from the Coimbatore district echo findings from various previous studies that have assessed the LULC change due to urbanisation and industrialisation. Various workers have reported the transformation of natural and rural landscapes into urbanised ones in the region, as a result of economic development, population growth, and changes in local and regional policies (Ustaoglu and Williams 2022; Wang and Salman 2023). In particular, the transformation of agricultural land into built-up as reported in our study has a very prominent pattern in developing countries such as India, Bangladesh and Pakistan (Rehman and Khan 2022; Das et al. 2022; Debnath et al. 2022).
Moreover, the decrease in agricultural land and forest coverage in our study is consistent with previous works that observed similar patterns in different regions. For instance, Mishra et al. (2020) also reported urban expansion at the expense of agricultural and forested areas in the Rani Khola watershed of the Sikkim Himalayas. Our results also align with the findings of Li and Song (2023), where shifts in agricultural practices and increased urbanization led to an increase in barren land, globally. The declining trend in forest cover, particularly in the western part of the study area that is encompassed by the western ghats, has been emphasised in several regional studies (Shimod et al., 2023; Gupta et al., 2023; Ramachandra and Setturu, 2023). These changes in land use and land cover shall have significant implications for biodiversity, local ecosystems, and climate change. The changes in the areal extents of water bodies, another critical aspect of land cover change observed in this, is likely due to same anthropogenic pressures, reflecting the growing demand for built-up areas and industrial use (Arif et al. 2023; Dhanya et al., 2023; Chisadza et al. 2023).
4.2. Climate Change
Figure 8 portrays the trends in annual temperature and rainfall in Coimbatore from 1985 to 2015. Data collection and analysis for this study involved daily rainfall metrics, culminating in an average annual rainfall of 678 mm. Insights derived from Fig. 8 reveal that 2002 and 2003 marked the driest years, while 2011 and 2012 were characterised by the most abundant rainfall.
Temperature exerts a significant influence over a region's climate, in turn shaping its socio-economic landscape (Sumathi et al., 2011; Wang et al. 2022; Cao et al. 2022). As Coimbatore resides within a tropical zone, it typically experiences high temperatures (Dhanya and Geethalakshmi 2023). Analysis of the monthly mean temperature data from 1985 to 2015 reveals an annual average temperature of 25°C. Importantly, Fig. 8 underscores an upward trend in temperature, with 2015 exhibiting a higher annual average temperature than 1985.
Rainfall was studied across four distinct seasons: Winter (December - February), Summer (March-May), Southwest Monsoon (June - August), and Northeast Monsoon (September - November). The study found that the Northeast monsoon delivers more than 50% of total rainfall. Rainfall during the Winter and Summer seasons was noticeably lower, with these two seasons experiencing the least precipitation. Comparative analysis of the periods 1985–2000 and 2001–2015 revealed changes in the seasonal rainfall distribution. In the more recent period, summer rainfall increased by 12%, while winter rainfall decreased by 6%.
Figure 9 delineates the monthly average rainfall and temperature in Coimbatore from 1985 to 2015, divided into two periods: 1985–2000 (first half) and 2001–2015 (second half).
A nuanced comparison between these two periods reveals that April has traditionally been the warmest month. During the Southwest Monsoon season, temperatures tend to gradually decrease and rise slightly thereafter, followed by a noticeable decline from October through January.
Post-2000 data provide interesting insights into the monthly rainfall trends. An overall increase in rainfall is observed in every month except for January, July, November, and December. These four months exhibit a contrasting decreasing trend in comparison to the pre-2000 era. This shift in precipitation patterns, combined with a general increase in temperature, could suggest impacts of climate change on the region's weather patterns.
Figure 10 provides a detailed depiction of the monthly and cumulative rainfall for the periods 1985–2000 and 2001–2015. The data suggest that the months from April to December are typically 'wet', receiving more than 200 mm of rainfall. On the contrary, January to March are 'dry' months, with rainfall less than 100 mm. October to December are the heaviest precipitation months, with rainfall exceeding 500 mm, hinting at potential flood risks during these months.
Table 3 Statistical Analysis of Monthly Rainfall and Temperature
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)
In Table 3, a p-value less than 0.05 establishes statistical significance in the correlation between monthly rainfall and temperature. This significant interrelation between the two parameters could be indicative of a changing climate in the region, with potential implications for agriculture, water resources, and urban planning.
In addition, urbanisation has also been observed to influence climate change at the regional level by increasing the land surface temperature, which in turn affects the surface air temperature. These changes adversely affect ecosystem services and human health (Jackson et al., 2013). Figure 11 illustrates the correlation between the land surface temperature and surface air temperature for the years 2000, 2009, and 2015 in the study area. In our study, a strong correlation (0.79) between land surface temperature and surface air temperature for the years 2000, 2009, and 2015 in the Coimbatore district supports the idea that urbanisation is associated with an increase in both land surface temperature and surface air temperature, a phenomenon known as the urban heat island effect (UHI) (Kanga et al. 2022; Fahmy et al. 2023). This positive association, as represented in Fig. 11, is indicative of the role of urbanization in climate change at the regional level. Furthermore, the statistical significance (p-value < 0.05) of this correlation suggests the robustness of this relationship.
The findings of this study are consistent with internationally established research suggesting that changes in weather patterns, including temperature and precipitation, are a consequence of climate change (Easterling et al. 2019; Pepin et al. 2022; Zhou et al. 2022). The increase in temperature aligns with broader trends of observed global warming trends over the same period, especially concerning tropical regions like Coimbatore, where high temperatures are already the norm (Shao et al. 2022; Dhanya and Geethalakshmi 2023). Studies have shown, how rising temperatures can significantly impact a region's socioeconomic landscape, affecting public health, agriculture, and various other aspects of human life (Wang et al., 2022; Cao et al., 2022).
The variability observed in the precipitation patterns in the study is also consistent with the patterns reported in various other studies. Regional rainfall distribution shifts and seasonal variability have been observed throughout the world and have often been linked to the manifestations of climate change (Trenberth 2011; Krishnan et al. 2020; Stendel et al. 2021; Jones et al. 2022). The increase in the variability of summer and winter rainfall, observed during the more recent period in this study, aligns with previous research pointing to shifts in seasonality due to global warming (Webb et al. 2013; Chen and Ford 2023).
The significant correlation found between temperature and rainfall in this study adds to the growing body of evidence that shows a complex interplay between these two, often influenced by atmospheric dynamics and topography (Tai et al. 2020; Huang et al. 2023a; Wu et al. 2022). For instance, higher temperatures can increase evaporation rates, potentially leading to more intense rainfall events (Huang et al. 2023a; Eum et al. 2023). The statistical significance of this relationship in the Coimbatore region highlights the need for further research to better understand and predict the impacts of such changes in precipitation and temperature.
4.3. Infectious Diseases
Health Impact Pathway
The World Health Organisation (WHO) predicts that climate change will influence human health via numerous pathways of differing complexity, scale, immediacy, and timing. This impact is subject to geographical variations, influenced by environmental and topographical factors, as well as the vulnerability of local populations (Fig. 12). In the near term, health impacts due to climate variability, such as anomalies in temperature and rainfall, are likely to be more significant than those due to long-term climate change. These anomalies can trigger vector-borne and water-borne epidemics before the weather returns to normal.
Local environmental conditions and socio-economic circumstances play a pivotal role in the health impact. Inadequate housing and rapid suburban sprawl are increasingly contributing to health risks due to communicable diseases and exposure to pollutants. Ganeshkumar et al. (2018) noted that laboratory confirmations of infectious diseases rose between 2011 and 2017, compared to studies before 2000, likely due to improvements in laboratory infrastructure.
Infectious Disease History and Preventive Measures
The Indian Council for Medical Research reports that during the 1950s, 1960s and 1970s, infectious diseases such as tuberculosis, leprosy, cholera, typhoid, plague, syphilis, malaria, polio, and smallpox were prevalent in the study area (Poonia et al., 2018). Despite aggressive disease control and prevention strategies, only smallpox and polio have been eradicated, in 1980 and 2014, respectively. Leprosy came under control in 2005, according to the state government of Tamil Nadu.
However, the incidence of infectious diseases, including vector-borne and water-borne diseases, increased as the population surged in the 1990s, as shown by the red line in Fig. 11.
Figure 12 presents the first reported incidence of dengue in 1998, according to the Indian Council of Medical Research.
Number of Reported Outbreaks
Outbreaks refer to sudden, significant increases in the number of infections or spillover beyond endemic areas, which can overwhelm public health resources and have a broader socio-economic and geopolitical impact. Rapid urbanisation and overcrowding in the past two decades have facilitated the transmission of infectious diseases, adding to the burden of the disease. Figure 13 illustrates an overall increase in infectious diseases caused by vector and water-borne pathogens from 2009–2015. Infectious diseases will persist as public health concerns due to the intertwined influences of ecology, climate, and human behaviour.
Urbanisation and Mosquito Breeding
The epidemiology of mosquito-borne diseases is shaped by increasing temperatures and urbanization, which disrupt mosquito micro-ecosystems (Makkar et al., 2018). Urban environments favour vector habitats due to fewer predators, diverse spatial constructs, secluded green areas, polluted water bodies by sewage, and a distributed human host population (Wilke et al., 2019). Increased urbanisation may also promote vector multiplication (Kumar et al., 2015). Figure 14 presents the breeding sites of the Culex and Aedes mosquito species in Coimbatore, as identified by Naresh et al., 2015.
Figure 15 presents the overlay of mosquito breeding sites on the built-up area of the study region, based on the findings of Kumar et al., 2015. The locations correspond to areas of high urban poverty and poor living conditions, such as Ambedkarnagar, Ukkadam, Kamarajapuram, which are close to the city centre of the study area (Sreebhagyalakshmi and Thomas, 2018).
Preventive and Control Measures
There is no specific treatment for dengue and chikungunya in the study area (Khan, 2016b). Only preventive measures exist to control the vector and infection. In the study area, people prefer Siddha medicine, including remedies such as papaya leaf juice, hill neem juice, and nilavembu water. Anti-adult measures like DDT, malathion, parathion, and fenthion; antilarval measures like temephos, Gambusia, Poecilia, and Labister fishes; and anti-bite measures like pyrethrum space spray and cyphenothrin-treated mosquito nets have been in use for the past two decades.
Furthermore, salmonella-induced diarrhoeal disease (waterborne), associated with temperature and demographic growth, is a major concern. Human-human and animal-human diseases have also gained prominence. Every summer, chickenpox spreads across the study region due to increased susceptibility and low vaccination rates among the population. Urbanisation intensifies influenza epidemics (Dalziel et al., 2018). Control measures implemented under the supervision of the state health ministry include personal prophylaxis, biological and chemical vector control, health education, community participation, environmental management, and source reduction methods (Khan et al., 2017).
Temperature's Effect on Vector and Pathogen Biology
Temperature dictates the characteristics of mosquito-borne pathogens and increases the transmission (Mordecai et al., 2019) of arthropod-borne viruses (Arboviruses). Increased human interaction in vector habitats invites anthropophilic mosquitoes, Aedes aegypti, Aedes albopictus, Culex quinquifasciatus, and Culex vishnui, which could lead to permanent endemic cycles of urban dengue and chikungunya (DENV and CHIKV). The optimal breeding temperature for Aedes spp is between 26℃ and 32℃ (Weaver & Reisen, 2010).
This study provides a comprehensive assessment of the impact of climate change and urbanisation on the incidence and transmission of infectious diseases in the Coimbatore district of Tamil Nadu. It depicts a profound relationship between climatic variables such as temperature and rainfall changes, and the proliferation of vector and waterborne epidemics. These findings align with the prediction on the complex and multilevel impact of climate change on human health (Patz et al. 2005; Swinburn et al. 2019; Barouki et al. 2021; Romanello et al. 2021).
The study elucidates the significant role of local environmental conditions and socioeconomic circumstances in determining health outcomes, according to the findings of Lastrucci et al. (2019), Ellena et al. (2020), and Asaaga et al. (2023). These factors, when combined with rapid suburban sprawl and inadequate housing, contribute to increased health risks due to communicable diseases and exposure to pollutants (Stevenson and Thompson 2019; Greenberg 2021; Giles-Corti et al. 2022).
The historical backdrop of infectious diseases in Coimbatore, as explored in the study, provides a context for understanding the current health challenges. Despite significant strides in disease control and prevention since the mid-20th century, the persistence of infectious diseases, including vector and waterborne diseases, has been exacerbated by the surge in population growth since the 1990s (Barrett and Armelagos 2013; Robson et al. 2022). The dynamics of disease transmission are further complicated by the intersecting influences of ecology, climate, and human behaviour.
The escalating trend of urbanization in Coimbatore has disrupted mosquito micro-ecosystems, thereby facilitating the transmission of mosquito-borne diseases. This assertion is substantiated by the findings of Gerken et al. (2023), Alenou et al. (2023), and Bondo et al. (2023) who have reported that urban environments create favorable conditions for vector habitats. These factors underscore the epidemiological implications of urbanisation and the need for strategic disease prevention efforts.
In terms of preventive measures, the study identifies a diverse array of strategies, including traditional remedies and state-supervised measures (Ogbulafor et al. 2023). The persistent threat of infectious diseases in the study area, such as dengue and chikungunya, highlights the importance of preventive actions, including vector control and health education, in mitigating health risks (Andrade et al. 2023; Menezes et al. 2023). The significant role of temperature in the biology of vectors and pathogens aligns with the findings of Ototo et al. (2022), Tahir et al. (2023), Meadows et al. (2023), and various other studies, reaffirming the impact of climate change on disease transmission.
This study tried to delineate the interwoven influences of urbanization, climate change, and socio-economic factors on the transmission and incidence of infectious diseases in the study area. The findings highlight the urgency of devising all-inclusive public health strategies that would respond to these dynamic factors that influence public health in the study area. Our findings also reinforce the need for ongoing research to better understand the complex interplay between urbanisation, climate change, and infectious diseases, to ensure the development of effective preventive and control measures.
4.4. Policy Recommendations
Enhanced surveillance of infectious diseases is crucial for early detection of outbreaks and to monitor the effectiveness of control measures. In this context, it is suggested to consider the establishment of robust health information systems to improve data availability and quality (Ranga et al. 2020). As climate change is projected to increase temperature and cause variations in rainfall, integrating climate change adaptation measures into public health policy and planning is also very crucial. In this context, the development of early warning systems for disease outbreaks linked to weather events is important to consider as developed in various regions of the world (Morin et al. 2018). Furthermore, the implementation of sustainable land use policies to mitigate the effects of rapid urbanisation must also be considered as a priority. Proper urban planning will reduce vector habitats, decrease pollution, and promote a healthier living environment (Zhang et al. 2023). Furthermore, more investment is needed in the health infrastructure sectors, especially in underdeveloped areas of the study area. The funding must be used to improve laboratory capabilities, improve health facilities, and improve access to improved health care. The government must legally encourage collaboration between different departments - such as health, environment, urban planning, and development - to tackle the multidimensional challenge of infectious diseases in the context of climate change and urbanisation. In addition, effective public health education programmes to raise awareness about infectious diseases and their link to climate change and land use are also very essential. Unless common masses related to these issues are sanitised, efficient overall prevention and mitigation cannot be achieved. This may also involve engaging communities in preventive measures to increase the effectiveness of preventive measures and ensure sustainable implementation. Fostering a conducive environment for research to better understand the interaction between climate change, urbanisation, and infectious diseases is also very important. Research must be aimed at both sanitising current strategies and developing innovative solutions for effective measures. These policy interventions have been framed to address the complex interplay of climate change, land use/land cover changes, and infectious diseases, and their implementation will require a coordinated and multifaceted approach at all levels of governance and broad stakeholder participation.