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Review

Evaluating the Measurement of Heat Stress in a Tropical City: Kolkata, India

by
Charles A. Weitz
1,* and
Barun Mukhopadhyay
2,†
1
Department of Anthropology, Temple University, Philadelphia, PA 19122, USA
2
Biological Anthropology Unit, Indian Statistical Institute, Kolkata 700108, India
*
Author to whom correspondence should be addressed.
†
Current address: Indian Anthropological Society, Kolkata 700019, India.
Climate 2026, 14(2), 47; https://doi.org/10.3390/cli14020047
Submission received: 29 November 2025 / Revised: 7 January 2026 / Accepted: 22 January 2026 / Published: 3 February 2026
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Abstract

People living in India are experiencing some of the hottest summers on the planet. Conditions are particularly harsh in Indian cities, like Kolkata, where high temperatures are combined with high humidity. Understanding how conditions in Kolkata have evolved could provide an important addition to the growing study of the problems facing megacities in the hot, humid tropics. Yet in Kolkata, this understanding is obscured by different, often incompatible, methods of assessing the intensity of heat stress. This narrative review considers the problems encountered when attempting to develop a clear understanding of past increases or even to quantify current conditions using conventional meteorological or remote sensing data. Rather than trying to arrive at a precise quantification of how much hotter it is now in Kolkata than in the past, we argue for more fine-grained, individual-level understanding of how heat is experienced. An example of this approach is provided by a study that used telemetric devices to continuously monitor the temperature and humidity to which elderly residents of slum areas in Kolkata were exposed during 24h periods as they went about their daily lives. This study indicates that individuals experience a diversity of heat conditions that are inadequately represented by outdoor temperatures. Living in dwellings where indoor temperatures are often hotter than outdoor temperatures, the daily heat stress experienced by this vulnerable group varies between conditions that are stressful but endurable to those that approach the limits of human heat tolerance. Given the likelihood of even hotter environments in the future, urban planners will need access to more comprehensive heat studies, focusing on continual monitoring of heat stress and physiological responses of individuals from different walks of life.

1. Introduction

There seems to be no doubt that human populations have been exposed to substantially elevated global temperatures over the past few decades [1]. This has led to increases in heat stress, particularly heat stress extremes, which can seriously compromise thermoregulatory responses, leading to hyperthermia [2]. The health consequences of core temperatures above 40 °C can be deadly, especially for vulnerable groups [3]. So, assessing the point at which external heat becomes unhealthy is of considerable modern concern. The evidence most commonly used to identify that point includes meteorological data from fixed land-based weather stations [4], heat indexes that are designed to alert people when temperatures approach or exceed dangerous levels [5], land surface temperatures (LST) estimated from satellite data [6], individual data on experienced heat conditions [7], and outcome data such as mortality or morbidity during extreme heat events [8]. The utility of each method will vary depending upon where people live, both because global warming has occurred unevenly [9,10] and/or because information at the local level may be incomplete, contradictory, or even absent [11]. Thus, it is useful to understand how each strategy performs in an area of the world where recent increases in heat stress have been severe.
Conditions suitable for such an examination exist in tropical megacities [12,13,14], particularly those in lower-income countries [15], where the urban heat island effect is intensified by high humidity [16]. A subset of these cities—those located in India—has experienced some of the hottest conditions on the planet [17,18,19,20,21]. Much attention has focused on recent temperature increases in New Delhi [22], where daytime highs soared above 40 °C for 32 days in 2024 [23], with some days even approaching 50 °C [24]. But temperature alone is often an insufficient indicator of heat stress [19,25]. In other Indian megacities—notably Kolkata—summer (March–August) temperatures may be somewhat lower than in New Delhi, but heat is exacerbated by high humidity, which can average near 80% [26]. Because atmospheric moisture reduces the evaporative heat-emitting capacity of human sweat, thereby elevating core body temperatures [27], the humidity experienced by Kolkata residents has been estimated to add the equivalent of 6.2 °C to the ambient temperature, creating a level of heat stress that was greater than in New Delhi between 2011 and 2023 [26].
This narrative review critically evaluates the ways in which the intensity of heat stress in Kolkata has been determined, both currently and over time. It first considers commonly used measurements of the environmental conditions. These include meteorological data from fixed land-based weather stations, heat index data, and LST data from satellites. It briefly evaluates the use of post hoc measures of heat stress such as mortality during heat waves and concludes by providing information about the way heat is experienced by individuals living in Kolkata, as a way of estimating the physiological responses to environmental heat stress [28].

2. Location and Description of Kolkata

Kolkata is located at approximately 22°30″ N (latitude), roughly the same latitude as Hong Kong and Dhaka in Asia, Havana in the Caribbean, and the northern islands of Hawaii in the Pacific (Figure 1). Because of its location near the Bay of Bengal, it has a tropical climate (Köppen climate classification Aw), with an annual rainfall of 1836.5 mm (72.30 in) [29]. The city of Kolkata covers approximately 206 km2 and is divided into 16 boroughs and 144 wards. It is managed and administered by the Kolkata Municipal Corporation (KMC). The KMC, along with parts of five other districts (Hooghly, Howrah, Nadia, North 24 Parganas, and South 24 Parganas), constitutes the Kolkata Metropolitan Area (KMA), which is approximately 1880 km2 in size. Based on 2025 estimates (Table 1), the KMC has the fourth largest municipal population in India [30], while the KMA has the third largest metropolitan area population in India [31] and the eighth largest metropolitan population in the world [32].

3. Measurement of Environmental Conditions in Kolkata

Heat stress is a physiological condition in which internal body temperature deviates from thermal neutrality. This can occur because of muscular activity and/or exposure to environmental heat. Measurements of environmental heat can thus provide an indirect assessment of the degree of heat stress. The most common strategy for assessing environmental heat is to measure meteorological variables hourly and then compute daily, monthly, or annual averages. This information is ubiquitously reported and therefore easy to obtain. But in Kolkata, different ways of analyzing meteorological data have made it difficult to arrive at a consensus about the level of heat, both in the past and in the present. Understanding why this has been the case requires knowledge of how heat has been measured, when it has been measured, where it has been measured, and the time periods used for comparisons. Differences in these parameters from study to study produce different conclusions about past and current heat stress.
To begin, an important dichotomy exists between heat measurements based on meteorological data from fixed, land-based weather stations and those based on satellite measurements of thermal infrared wavelengths. The following consideration of these two approaches is based on recent studies, identified using Google Scholar, PubMed, Scopus, Web of Science, CORE, and Science.gov. Key words used were “Kolkata”, in various combinations with “heat”, “heat waves”, “heat stress”, “heat index”, “land surface temperature”, “temperature”, “humidity”, and “summer”. While it may not include every study that has reported heat conditions in Kolkata, it is likely a representative sample that includes most of them. All studies that were accessed in this process are included in this review so readers can understand how the varied approaches have made a consensus regarding past and present heat stress difficult to achieve.

3.1. Studies Based on Meteorological Variables Measured at Land-Based Weather Stations

The advantage of using meteorological data from fixed, land-based weather stations is that they are relatively easy to obtain. Information on past temperatures in Kolkata, for example, is available from sources such as the Indian Meteorological Department [35], Weather Underground [36], and Meteoblue [37]. The disadvantage is that there are relatively few fixed stations in the Kolkata area that offer long-term meteorological data: primarily Alipore, located in a park area of southern Kolkata, and Dum Dum, located in a built-up area of northern Kolkata near the international airport. While providing considerable time depth, data from these two stations cannot characterize the variable conditions that exist in the complex environments of the KMC or KMA. Fixed automated weather stations in Kolkata offer greater coverage, but they are of relatively recent origin and provide little time depth [38].
The studies listed in Table 2 organize meteorological data in one of two ways: either by presenting a single average value for heat measurements made over several years (Part A), or by presenting multiple heat measurements made over time (Part B). Several studies use both approaches and are included in both parts of Table 2. The studies listed in Table 2B present ambient temperature (referred to as “Tdb” (dry bulb temperature) in several different ways: average monthly Tdb [39,40], average seasonal Tdb [26,41,42], seasonal Tdb extremes [43], maximum Tdb during heat waves [44], and average annual Tdb [40]. One study (40) indicates that the average annual Tdb increased significantly between 1902 and 2021. The remaining studies analyzed changes in Tdb during the summer, presumably the time when the hottest conditions would exist. While these studies report that temperatures fluctuated from year to year, they present very little compelling evidence of a consistent temporal change. Unfortunately, results from these studies cannot be concatenated, since in addition to differences in the way temperature was measured, the measurement periods varied (March–August [26,42], March–May [41], April–July [43,44]). More concerning is the fact that none of these studies indicate how the averages were determined. The best practice would be to average hourly data every day for however long the measurement period might be [45,46]. For example, for a single year, the average would be 365 days × 24 h = 8760 hourly temperature measurements. While this may have been the way the averages were computed in the studies listed, there is no evidence that they were. Many may have been computed simply as the average of daily high and low temperatures, which is a common practice that produces a biased temperature estimate [47]. Taking the data reported in these studies at face value and disregarding the problems noted above, it could be argued that recent changes (the last 20–30 years) have been “nominal”, while the evidence over a much longer period (100+ years) suggests an upward trend. A more cautions appraisal would be that these studies have not provided a satisfactory perspective on whether, or the extent to which, the ambient temperature in Kolkata is hotter now than in the past.

3.2. Studies Using Meteorological Variables Measured at Land-Based Weather Stations to Compute “Heat Indexes”

Meteorological data have been used to produce over 100 so-called “heat indexes” [5], at least 13 of which have been used in studies of heat stress in Kolkata (see Table 2). These indexes use an array of algorithms, often based on different sets of meteorological variables, to calculate “temperatures” that are equivalent to the thermal conditions associated with different levels of “risk” for either hypothermia or hyperthermia. Not only are these “equivalent temperatures” calculated differently, but they are not the same as, or comparable to, ambient temperatures—even though they are presented in °C. Furthermore, they are not mutually comparable, since they often identify different “equivalent temperatures” at which life-threatening effects occur [57,58]. The problems applying heat indexes can be illustrated by considering the Physiological Equivalent Temperature (PET) used in several of the studies listed in Table 2 [48,49,51]. PET is representative of heat indexes that base categories on models of human thermoregulatory physiology. It defines a set of nine categories (from “extreme cold stress”, through “no thermal stress” to “extreme heat stress”), the ranges of which are defined by a physiological model that predicts how an “average person” will respond to a combination of air temperature, vapor pressure, wind velocity, and mean radiant temperature [59,60]. The “average person” may vary. In one study conducted in Kolkata, this individual was conceptualized as a standing male, 35 years of age, 1.75 m tall, weighing 75 kg, and wearing clothes with an insulation value of 0.6 clo [49]. Of course, such a model may not represent the responses of any single individual, since personal differences exist in both thermoregulatory physiology and physical characteristics [61]. For example, levels of thermal stress based on the “average person” noted above would not apply to middle class men, ages 60 and older (n = 152) living in Kolkata (height = 1.685 m, weight = 63. 9 kg), or to older, middle class Kolkata women (n = 132), who were 1.517 m tall and weighed 59.1 kg [62]. They would apply even less to older women living in Kolkata slums (n = 421), who averaged 1.445 m tall and weighed 49.89 kg [63], and were doing more than simply standing still [7].
The point is that, while PET and other heat indexes are useful as general guides to help the public recognize heat conditions that may be dangerous, they are not meant to predict individual responses. A second problem is that the ranges of thermal categories for most indexes, including the identification of life-threatening heat, are not verified by empirical studies. For example, the “Heat Index”, used by a number of the studies listed in Table 2 [26,39,42,52,53,55] and also by the US National Weather Service, was the subject of a court case in the US, which found that there was no scientific basis for the risk categories (“Caution”, “Extreme Caution”, “Danger”, “Extreme Danger”) commonly displayed on “Heat Index” charts [64].
The confusion caused by using different heat indexes in Kolkata is exacerbated by the same problems encountered when comparing studies of ambient temperatures. Some studies present heat index data as monthly averages [39], while others present seasonal averages [41,42], or seasonal heat extremes [43], or annual averages [26,42,51,52,56], or the number of days per year that a particular heat measurement exceeds putative “dangerous” levels [39,42,51,54,56], or the number and duration of heat waves [44], or a determination of when increases in summer temperatures became significant [53]—all over a varying number of years. Several heat index studies indicate that some degree of warming has occurred over time [40,41,51,53]. But estimates of the degree to which this occurs differ among the studies, depending on the time period covered and the heat index used. Arriving at a consensus about heat increases is further complicated by other studies that report little or no difference over time [39,42,43,55]. As has been noted elsewhere [65,66], some of this ambiguity is likely related to the type of measurement (maximums, minimums, or averages), and to the time period used as the frame of reference (daily, seasonally, or annually). In Kolkata, heat index studies reporting annual averages tend to show smaller temporal differences than those reporting seasonal or monthly averages.
The clearest indications of increases in hot weather come from studies that report maximum heat index levels, number of days a heat index measurement exceeds dangerous levels, or the number and duration of heat waves [42,44,52,54,56]. This effect is illustrated by a study of changes in two heat indexes: Net Effective Temperature (NET) and the Thermo-Hygrometric Index (THI). Between 2020 and 2022, the number of days NET > 34 °C (classified as “very hot”) increased from 188 to 197 and the number of days THI > 30 °C (classified as “torrid”) increased from 37 to 70 [56]. Paradoxically, related to the point made above, the annual averages for both NET and THI over the same period show no statistically significant differences [56]. Over a longer period, between 1991 and 2021, annual averages for both measurements show considerable yearly differences, but no clear positive or negative trend [56].

3.3. Studies of “Land Surface Temperatures” (LST), Based on Satellite Measurements of Thermal Infrared Wavelengths

Since the 1960s, satellite measurements of thermal infrared wavelengths have been used to generate so-called Land Surface Temperatures (LST) [67,68]. Satellite-based LST data have the advantage of providing more systematic coverage of large and variable areas that may be only sporadically covered by land-based monitoring stations. A rough analogy is that LST studies provide fine-grained “snapshots” of heat conditions since they are generated by single satellite passes during a single day, while studies of land-based weather stations provide longer-term “movies” that are often out of focus because of the problems (noted above) associated with longitudinal data analysis.
Despite their apparent advantages, there may be significant problems using LST data to characterize heat conditions over extended periods. These include gaps in the times that a satellite passes over the area of interest, the infrequency of clear skies, differences in the resolution of satellite images, and variable capacities of detecting differences between different urban features [69]. To these might be added technical concerns, such as the use of different algorithms to transform thermal infrared data into land surface temperatures [70], and the existence of different methods used to validate those data [68]. Although these problems can be addressed in a number of ways [69,71], they are particularly acute for urban areas [72].
Table 3 provides a summary of LST studies conducted in Kolkata. It should be noted that most of these studies were designed to examine the LST differences between densely built urban areas and natural environmental spaces—so-called “green” zones (agricultural land, fallow land, water bodies, areas with trees, etc.). Nevertheless, several differences in the way the studies in Table 3 were conducted are immediately obvious. First, coverage varies. Some studies are concerned only with the KMC area, while other studies encompass the entire KMA (See Figure 1). Second, many of the LST studies use satellite passes made during the winter, since this is a time when cloud cover is minimal. So, information about maximum heat is not always available. Third, many studies have limited temporal depth (LST is measured on only one or two days) and thus cannot be used to understand heat changes over time. Fourth, the geographic coverage of the area differs between studies as does the percent of urban versus “green” spaces. Table 4 shows that inter-study estimates of the size of built and non-built parts of the KMA vary considerably during the same years, as well as over time. This seems to be related to the different ways that the boundaries of the KMA have been identified from the satellite images and the way infrared wavelength differences have been used to identify the size of built and non-built parts of the area. Thus, like studies based on fixed, land-based weather stations, the variable conditions under which LST data are analyzed make it difficult to arrive at a consensus about changes in LST over time.
Two further concerns limit the use of LST to assess heat stress in Kolkata—or any other city. First, direct solar radiation absorbed by the built environment during hot weather can increase the LST by several degrees centigrade over air temperatures [72,94], a condition noted in Kolkata [54]. This makes the differences between LST and air temperatures measured by land-based weather stations greater in urban areas than in more “green” spaces [72,94]. Second, by providing information on temperatures alone, LST does not capture other climatic factors that intensify heat stress, such as solar radiation, wind speed, and, of particular significance for Kolkata, humidity [95].
Several studies report LST changes over time but base their conclusions on satellite passes made on a single day in different years [54,78,81,82,83,87,92]. This restricts their ability to indicate temporal changes in LST, since the extent to which a single day can characterize an entire season or a whole year is limited. Two studies [88,89] seem to be most appropriate for determining changes in hot weather. These are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data which has the advantage of more frequent coverage over time but has relatively coarse spatial detail (1 km) [69]. Nevertheless, both of these studies provide strong, internally consistent evidence for an increase in LST in KMA over time.
All studies attribute increases in LST in the Kolkata area to the dramatic population growth that has taken place in both the KMA and KMC (see Table 1). Several studies also link LST increases to an equally substantial increase in the geographic extent of the urban built environment, along with a simultaneous reduction in “green” cover regions [54,75,76,77,78,83,87]. All studies also agree that the urban, built zone is hotter than surrounding “green” areas, and studies with temporal depth (e.g., refs. [42,75,76,85]) indicate that the summertime LST in built areas has significantly increased over time. This is attributed to the well-recognized urban heat island (UHI) effect [96]. Heat from solar radiation is absorbed and retained by impervious surfaces (paved roads, parking lots, building materials, etc.), and intensified by city morphology (building density, building heights, canyon width, etc.) which retards cooling by obstructing airflows [88]. This is further intensified by anthropogenic heat sources, such as vehicular emissions, air-conditioning heat output, industrial emissions, and human metabolism [84], along with heat generated by new technologies, such as the use of rooftop photovoltaic solar panels [97].
Several LST studies of Kolkata city have indicated that differences in building density and surface types (building materials, roads, roof types, etc.) create conditions that are considerably hotter in some areas than in others [70,73,77,90,91,92,93]. The degree to which LST has increased in different types of “green” spaces (i.e., water bodies, agricultural land, fallow land, forests, open land, park land, etc.) also varies [75,76,78,79,81,83,85,90]. Nevertheless, all “green” areas also seem to be undergoing an increase in LST, but not as dramatically as the increases noted in built areas [54,74,81,88,90]. The existence of differences in the severity of heat stress within the urban built area, as well as differences between “green” area categories, make it clear that attempts to characterize LST using a single value for the city or the metropolitan area inadequately represents the heat stress endured by individual Kolkata residents—particularly those living in hot, closely compacted low-rise structures characteristic of slum areas [93].

3.4. Using Mortality Data to Gauge Heat Stress

Given the relationship between heat and mortality [98], it might seem reasonable to use information on heat-related deaths as a post hoc indication of the severity of heat stress. Unfortunately, problems with death registration and death attribution make precise determination of the number of people who die during hot weather difficult, and likely results in an underestimate of heat-related mortality [99]. In India, attempts to determine mortality due to extreme heat have generally been based on national data and are complicated by underreporting [100,101], by variations in death registration from district to district [102], and by different numbers of heat-related deaths reported by different Indian government agencies [103]. Furthermore, heat-related mortality is often equated to deaths from heat stroke (e.g., [104]), and does not include deaths from existing morbidities (including cancers, respiratory diseases, cardio-vascular diseases, diabetes, etc.) that are known to increase during hot weather [105]. Notwithstanding these problems, analysis at the national level indicates that mortality may increase 9% per degree centigrade when daily mean temperatures increase above 35 °C [106].
Few studies have attempted to characterize heat-related deaths in Kolkata. A single rigorous study involved an estimate of so-called “excess deaths”, based on a comparison of deaths during heat waves relative to deaths during the same period in other years when temperatures were “normal” [107]. This study estimated that an average of 172 “daily deaths” in Kolkata were attributable to heat waves between 2010 and 2019. Since Kolkata averaged 3.0 heatwaves per year and 4.2 days per heatwave [107], this would yield 21,672 deaths per year (172 × 3.0 × 4.2)—roughly 4.4% of the annual heat-related deaths (489,075) estimated to have occurred worldwide during the same period, and within the range modeled for South Asia [108]. This figure represents “all cause” mortality, and therefore includes deaths related to a variety of morbidities that were exacerbated by heat stress, in addition to those directly attributable to heat stroke. This means that the heat-related mortality rate in Kolkata was an astonishing 3295 deaths per million residents (based on the 2025 population estimate, see Table 1). By comparison, heat-related deaths in Italy (the highest in Europe) reached 295 per million residents in 2022 [109]—an extremely hot year, but not as hot as in Kolkata. While high, the estimated number of heat-related deaths in Kolkata is in line with estimates for other Indian cities [107], and with mortality predictions for the future. By the summer season of 2080, it is estimated that between 25,900 and 35,600 annual heat-related deaths will occur in Kolkata, depending on the degree to which temperatures continue to rise and the degree to which effective individual and governmental heat-accommodation strategies exist [110]. In a “worst case” scenario, an estimated 53,300 deaths could occur—the highest prediction for any city in India. These numbers suggest that heat has inflicted considerable suffering among Kolkata’s residents, and that experienced heat levels are likely among the most severe in India and perhaps the world.

4. Using Individual Data to Understand the Severity of Heat Stress Among Kolkata’s Residents

The lack of a standardized approach to collecting and analyzing heat and heat-related data in Kolkata has contributed to different perceptions of the current level of stress, relative to what might have existed in the past. Paradoxically, this exists in direct contrast to the vivid portrayals of suffering that appear in the local media [111]. This contrast raises a much more immediate and pressing concern than trying to arrive at a precise quantification of how much hotter it is now in Kolkata than in the past: How are people living in the city affected? Answering this question requires a shift in perspective from the study of general environmental measures of heat stress to a focus on the way individuals experience heat stress. This shift has been recommended by others [112] and is, after all, the direct way of determining heat stress in the physiological sense.

4.1. Measurement of Physiological Responses During Heat Exposure

The direct method of determining heat stress would be to monitor the thermoregulatory physiological responses of individuals during external heat exposure. Sampling multiple individuals would provide essential information on how differences in age, gender, and activity affect responses to the same external conditions. At a minimum this would involve measuring sweat production and evaporative heat loss as an indication of the capacity of the body to eliminate heat, heat rate as a measure of circulatory capacity to move heat from the core to the skin surface, and core temperature as a measure of how effectively circulatory and sweat responses are able to maintain internal heat homeostasis [113]. These responses are commonly monitored in laboratory studies in controlled environments [114,115,116], where it is possible to continuously evaluate core temperature using technology like telemetric gastrointestinal pills [117]. Currently, however, it is impractical to use devices that directly monitor core temperature in studies that involve large numbers of individuals going about their daily activities over the course of a day or more. Non-invasive indirect monitoring of core temperatures is possible using monitors that estimate internal heat based on skin temperature and heart rate, e.g., [118], but this technology is currently expensive. Consequently, no study assessing a complete array of thermoregulatory responses among individuals exposed to heat during their daily activities has been conducted in Kolkata, or, far as we know, in India.
There are, however, a few studies that have monitored either heart rate or evaporative heat loss from the skin among workers during the summer in the Kolkata area. Two involve short-term monitoring of heart rate: one among female brick workers in the KMA [119], and another monitoring heart rate among rice agricultural workers in an area just outside the KMA [120]. In both cases, heart rate was monitored for 30 min, beginning at a resting state. During April and May, when temperatures ranged between 35 °C and 40 °C and humidity varied between 90% and 95%, the average heart rate of brick workers increased to 130 b/m and was still rising when the measurements stopped. Among agricultural workers monitored when air temps were between 35 °C and 36 °C, average heart rate likewise increased to over 130 b/m. In the case of the brick workers, who were in their early 20s, 130 b/min represents roughly 55% of estimated maximum heart rates. But among the agricultural workers, who were 25–35 years of age, 130 b/m represents between 65% and 70% of maximum heart rate. While these levels are associated with “moderate cardiovascular stress”, it must be remembered that heart rates were still rising when monitoring ceased at 30 min and, in both cases, would probably have continued to increase until workers became exhausted. Therefore, continuous monitoring of heart rates past 30 min would likely have identified a very high maximum level, indicative of severe cardiovascular and thermoregulatory stress.
A final study monitored evaporative heat loss from the skin, also among agricultural workers near KMA [57]. In this study, evaporative heat loss increased from 200 W/m2 in the thermally neutral state to over 800 W/m2 during 8 to 9 h of agricultural work in ambient temperatures over 35 °C. An evaporative heat loss of 800 W/m2 indicates significant heat stress—one that greatly exceeds levels observed in controlled laboratory tests, e.g., [121]. It reflects a substantial reduction in the core-to-skin temperature gradient, thereby making it harder for the body to dissipate internal heat. Not surprisingly, this study reported that the increase in evaporative heat loss between the coolest and hottest months was proportional to an increase in heat-related symptoms.
Failing direct measures of physiological responses, the level of heat to which individuals are exposed can also be used as an indicator of stress. In Kolkata, studies using the latter strategy fall into three general categories. The first involves so-called “thermal comfort” studies, which attempt to determine how individuals living in, or exposed to, everyday indoor or outdoor conditions perceive the level of heat they are currently experiencing on a Likert scale [122,123]. The second involves the measurement of heat conditions that exist in various indoor situations. The third involves continuously monitoring the heat experienced by individuals as they go about their daily lives.

4.2. Thermal Comfort Studies

Only a small number of studies using the “thermal comfort” approach have been conducted in Kolkata. In outdoor markets in the city, a study of 318 respondents (192 during the summer and 126 during the winter) determined that the upper limit of the “neutral” range (i.e., no discomfort) based on a combination of winter and summer conditions was Tdb = 27.56 °C, while the preferred temperature was Tdb = 29.44 °C [124]. Likewise, our study of outdoor and indoor conditions experienced by 130 elderly men and women in Kolkata slums indicated that Tdb = 27.67 °C was the temperature at which there was at least a 90% probability of reporting comfort [125]. A third study conducted at 41 outdoor locations where the average Tdb = 30 °C in March indicated that all of the respondents found the temperature to be above the “neutral” range [126]. However, another study [127], conducted at several outdoor locations during the summer indicated that the preferred temperature of 200 respondents was much higher (Tdb = 33.2 °C). On the whole, these values appear above those reported for people living in temperate environments, possibly a consequence of long-term acclimatization to extreme heat [58]. The possibility that acclimatization might provide some thermoregulatory compensation in cases of life-long extreme heat exposure has received little or no attention, but it seems well worth investigating.

4.3. Indoor Heat Studies

The measurement of indoor heat can be used to assess the conditions experienced by occupants—at least during the times that they are present. Presumably, the hotter the indoor temperatures become, the greater the heat stress will be. Several studies have compared indoor temperatures in older buildings (mostly constructed in the 19th and early 20th centuries, and characterized by thick walls, interior courtyards, large, shaded windows, high ceilings, and insulated roof structures) and newer buildings (mostly multi-storied buildings constructed since 2000, that were framed with reinforced cement, with thin exterior walls, small windows, low ceilings, and a cement slab roof) [128,129,130,131]. During the summer (April–June), interior spaces in the former were cooler (average indoor HI = 36.51 °C) than interior spaces in the latter (average indoor HI = 38.37 °C), which were also hotter than average outdoor HI (HI = 37.08 °C) [110,111,112]. Hotter indoor compared to outdoor temperatures were reported by another study of newer buildings, with little cooling during nighttime hours [132]. These studies also point out that rooms on upper floors (particularly those just beneath the roof) are invariably hotter than those on lower floors [130,132,133]. The extremely hot surface temperatures of concrete slab roofs (up to 67 °C), along with frequently closed windows limiting ventilation, and the use of ceiling fans that blow hot air down on the occupants cause intolerably hot conditions in the upper floors of newer, multistoried buildings [130]. During summer afternoons, the HI of rooms on the top floors of modern buildings can be between 2 and 7 °C hotter than rooms on lower floors. Even overnight and in the early morning, rooms on the top floor are still 2–4 °C hotter than those on lower floors [133].

4.4. Continuous Monitoring of Experienced Heat Conditions: A Case Study

Our study of elderly living in Kolkata slums also monitored indoor conditions [134], but with an important additional variable: a continuous measurement of the heat experienced by individuals during 24h periods [7]. This is the only study in Kolkata to date that provides an indication of the level and variation in heat exposure while individuals go about their normal daily activities—both indoors and outdoors—and only one of two that have been conducted in India (the other, [135]). It took place in slums located in two boroughs that have been identified as the hottest (Borough 2) and third hottest (Borough 4) in the city, based on LST data [93]. These areas are characterized by compact low-rise dwellings, structures that have the highest mean LST in the city [73]. Heat inside the dwellings is intense, due to the absorption and retention of solar radiation by construction materials (cement or brick walls, with corrugated iron, cement, or tile roofs). Our study indicated that interior heat was exacerbated by small dwelling size (74% consisted of a single room, most of which were <200 ft2 in size), indoor cooking (using either kerosine metallic cookstoves or earthen cooking ovens using coal or cow-dung patties for fuel), appliances (refrigerators, televisions and radios), and body heat generated by as many as seven residents [134]. None of the dwellings had air conditioning. Windows were small, sometimes absent (6%), and along with doors, were invariably closed at night. As a result, there was very little cooling during the overnight hours.
Figure 2 shows the median 24 h Heat Index (HI) values that were recorded in 123 dwellings between May and August in 2019, compared with the median HI values recorded at Alipore IMD station over the same 24 h periods. HI was used because it represents a composite measure of both heat and humidity. Both the median indoor and the median outdoor temperatures were determined from 24 hourly measurements. Over the range of 24 h HI values recorded outdoors during the summer period, median indoor HI values averaged around 5 °C hotter than median outdoor HI values.
The severity of the heat stress experienced by individuals was documented using telemetric devices. These devices were worn by participants and continuously monitored the HI they experienced during 24 h study periods [7]. Figure 3 shows the hours during the 24 h study periods that individuals experienced HI values equal to, or greater than, 41 °C (106 °F)—a level that is described as “dangerous” in HI charts—compared to simultaneously measured 24 h median outdoor and indoor HI conditions. Since most of the elderly participants spent most of their time indoors, it is understandable that indoor HI is a much better predictor of daily “dangerous” heat exposure than outdoor conditions recorded at Alipore IMD station.
These data cover the range of indoor, outdoor, and experienced conditions that existed over the course of a summer in Kolkata (May–August 2019). Since the study was conducted nearly every day during this period, the data in Figure 3 provide an estimate of the time that any resident of Kolkata slums might experience HI ≥ 41 °C, relative to daily outdoor and indoor heat conditions. Thus, even on days when the 24 h median outdoor HI is relatively low (i.e., <35 °C), the relationship depicted in Figure 3A suggests that slum dwellers will experience up to 8 h during which the HI ≥ 41 °C. Perhaps more importantly, Figure 3B indicates that when median indoor HI levels exceed 35 °C, conditions in slum dwellings become so hot that an astonishing number of people are likely to experience HI levels ≥ 41 °C for nearly the entire day (20–24 h: 74 out of 130 = 57%). Of course, this assessment only functions as a guide, since cross-sectional data will not always represent longitudinal data accurately. Furthermore, it provides an estimate of the risk of experiencing significant heat stress during the summer among individuals living in Kolkata slums and cannot be applied to everyone living in the city.

5. Mitigating Heat Stress

The value of understanding individual responses to the heat, whether based on physiological measurements or on experienced conditions, lies in the fact that they provide a more nuanced indication of variable exposure and stress. Single measurements provided by “macro” weather information (i.e., ambient temperature, heat indexes, or LST data) may tempt planners to justify a single response. Individual data requires planners to prepare different responses that address different levels of stress in different individuals. This seems to be the approach guiding the development of the KMC’s heat action plan [136]. This plan includes several ambitious projects designed to “reduce local temperatures, improve urban resilience, and protect vulnerable populations from extreme heat”. Among the goals are increasing green cover, implementing cool roof technologies, promoting permeable surfaces, and establishing cooling centers. All these have been recommended in one form or another by studies that have been conducted in Kolkata and other tropical cities, e.g., [43,73,96,132,137,138]. There is, however, debate as to their potential effectiveness. For example, some simulation studies in Kolkata [137,138] suggest that cool roofs and other mitigation technologies could provide a small (1–2 °C) but important benefit. Other simulations indicate that none of the cool roof or cool pavement strategies may offer any significant reduction in air temperatures [139]. Even presuming that heat-resistant building construction modifications might be theoretically effective [132], it does not seem that they are being implemented in any systematic manner.
The information included in this review clearly indicates that general heat mitigation strategies may not be equally effective in all parts of the urban area. For example, a strategy aimed at the extreme heat that characterizes dense concentrations of low-rise buildings (as in slum areas) might differ from that applied to areas where more green cover exists. This suggests that multiple strategies targeted for different areas and specific groups would enjoy more success than a “one size fits all” approach. But multiple approaches tailored to individual sections of the city will be expensive and must compete for funding with solutions to other environmental problems—most notably flooding in low-lying city areas adjacent to the Hooghly River [140,141]. One possible strategy to overcome this problem would be to structure mediation efforts on a triage basis. Based on both LST data and our study, slums are the hottest locations in Kolkata. Given that over one-third of the city’s population live in slums, an initial focus on those neighborhoods may be the best way to begin. Perhaps the place to initiate construction related to increasing green cover, implementing a cool roof plan, or constructing cooling centers would be in these areas.
Because the heat mitigation strategies available to the KMC appear to be limited, as is the case in other tropical urban areas [137], individuals likely will be left to cope as best they can. Among those who can afford it, air conditioning has become a necessity. However, this technology is both expensive and requires considerable energy subsidies [142]. Nevertheless, air conditioning is being used by a growing number of individuals, potentially causing a significant strain on the city’s power grid [143]. We have even observed that some residents of slums in the southern part of the city have begun to install and use air conditioners, despite the generally poor finances of most of Kolkata’s slum dwellers [144]. For the vast majority of slum residents, however, coping strategies are meager [125]. Almost everyone uses overhead fans. But they provide little or no relief in hot and humid conditions [145] and simply circulate hot air from the top of the dwelling down on the people living at the bottom. Water is copiously consumed, since the KMC has provided taps for the supply of potable water during different hours of the day in each slum settlement. All households served by such a tap collect water and store it for regular use. Other strategies are practiced with considerably less frequency. This includes changing or removing clothing (46.7%), moving to a cooler place (39.8%), avoiding or reducing activities (5.3%), and taking a shower, bath, or sponge bath (3.0%). Thus, most of the elderly participants in our study had little recourse but to endure the heat, regardless of its intensity.
It is not surprising that the combination of excessive indoor heat and the meager mitigation strategies available to elderly living in Kolkata’s slums leads to high frequencies of heat-related symptoms. Table 5 lists the common heat-related conditions reported by the elderly who participated in the study. The frequency of these symptoms appears to be roughly the same as reported for residents living in rural areas near Kolkata [57,125], but possibly somewhat higher than described for a sample of West Bengal residents during a heat wave [146]. Unfortunately, there is no evidence that these surveys were conducted using stratified representative sampling methods. Thus, they cannot be used to provide a serious test of the hypothesis that the greater heat endured by the urban poor living in slums produces a correspondingly higher incidence of heat-related morbidities [147,148,149,150]. While there is every reason to believe that this is the case [148], such a test would require systematic and accurate reporting of such conditions in different segments of the population—something that should be a goal of future research [148].

6. Conclusions

Kolkata is a tropical city that, throughout most of its history, has been plagued by disorganized development. This development has been the focus of one (perhaps the only) item on which all local heat studies agree: Increases in hot conditions (to whatever extent they have occurred) have been caused by the unregulated growth of urban, built areas and the simultaneous reduction of “green” spaces. The effect this has had on the heat stress to which residents are exposed (i.e., current and past heat levels) seems to escape consensus. Different studies use different methods of determining the level of heat (e.g., land-based data vs. LST data, use of air temperature vs. heat indexes, etc.); they report measurements that are averaged for different time periods (daily, monthly, seasonally, or annually); and they report comparisons made over different numbers of years. Consequently, some studies indicate that heat levels have increased, while others report little or no changes over time.
LST studies clearly show that differences exist between, as well as within the urban built environment and “green” spaces. In terms of heat experienced by residents of the city, differences in construction, construction materials, and construction density lead to significant differences in local heat intensities. Kolkata’s slums seem to experience the hottest conditions in the city, which are considerably hotter than daily averages monitored at local IMD weather stations. Thus, using a single metric (such as average daily, monthly, seasonal or annual temperatures, heat indexes, or LSTs) does not reflect the diversity of heat stress experienced by the city’s residents. Morbidity and mortality information would be extremely useful in assessing the impact of heat stress in Kolkata. But in Kolkata, these data are currently unreliable, even when they are available. The “gold standard” for understanding the level of heat stress in Kolkata’s population would be to conduct studies of physiological responses, but such studies have yet to be conducted. Until they are, we argue that continuously monitoring the heat that individuals experience in their daily lives can provide a useful guide to understanding the diversity of stress that exists among the city’s residents. As an example, our study of elderly slum residents shows that their heat exposure varies depending primarily on indoor conditions rather than daily outdoor temperatures. However, even on relatively cool days, they endure hours of heat stress that approach the limits of human thermoregulatory capacity. Further studies of the same type could significantly enhance our understanding of the “overlooked” humid heat burden facing residents of slums worldwide [150], regardless of uncertainties in the quantification of outdoor heat.
To its credit, the KMC has developed a heat action plan. Currently, however, this plan has only been released to stakeholders for consultation prior to implementation [151]. In a city where there are so many environmental, infrastructural, and social conditions that demand attention and money, establishing a solution that reduces heat stress for all residents is likely unattainable. Recognizing this, relying on macro “fixes” may not be the best strategy—not only for Kolkata, but for all similar cities in hot, tropical environments. Instead, a set of micro solutions targeted in a triage manner for areas most severely affected by heat may be the only effective way forward.

Author Contributions

Conceptualization, C.A.W. and B.M.; Writing—Original Draft Preparation, C.A.W.; Writing—Review and Editing, C.A.W. and B.M. All authors have read and agreed to the published version of the manuscript.

Funding

American Institute of Indian Studies, Temple University, Philadelphia, PA, USA.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to the ongoing use in the creation of new publications.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (A) Location of West Bengal (blue) in India; (B) Location of Kolkata Metropolitan Area (red) in West Bengal; (C) Location of Kolkata Municipal Corporation (green) within Kolkata Metropolitan Area.
Figure 1. (A) Location of West Bengal (blue) in India; (B) Location of Kolkata Metropolitan Area (red) in West Bengal; (C) Location of Kolkata Municipal Corporation (green) within Kolkata Metropolitan Area.
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Figure 2. The 24 h median indoor Heat Index in Kolkata slum dwellings compared to 24 h median outdoor Heat Index based on temperature and humidity recorded at Alipore IMD station. Linear regression R2 = 0.596.
Figure 2. The 24 h median indoor Heat Index in Kolkata slum dwellings compared to 24 h median outdoor Heat Index based on temperature and humidity recorded at Alipore IMD station. Linear regression R2 = 0.596.
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Figure 3. Hours that individuals experienced HI ≥ 41 °C relative to 24 h median outdoor HI recorded at Alipore IMD station (A), and 24 h median indoor HI recorded in their dwellings (B). Outdoor conditions are a poorer predictor of the hours that individuals had to tolerate “dangerous” HI levels (R2 = 0.430) compared to indoor conditions (R2 = 0.711).
Figure 3. Hours that individuals experienced HI ≥ 41 °C relative to 24 h median outdoor HI recorded at Alipore IMD station (A), and 24 h median indoor HI recorded in their dwellings (B). Outdoor conditions are a poorer predictor of the hours that individuals had to tolerate “dangerous” HI levels (R2 = 0.430) compared to indoor conditions (R2 = 0.711).
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Table 1. Population of Kolkata Municipal Corporation (KMC) and Kolkata Metropolitan Area (KMA) between 1971 and 2025.
Table 1. Population of Kolkata Municipal Corporation (KMC) and Kolkata Metropolitan Area (KMA) between 1971 and 2025.
YearKMCKMA
19713,727,020 [33]7,488,000 [34]
19814,126,846 [33]9,289,000 [34]
19914,399,819 [33]11,176,000 [34]
20014,572,897 [33]13,278,000 [34]
20114,496,694 [33]14,086,000 [34]
2025 (estimate) 4,631,392 [34]15,845,000 [34]
Note: Table follows Reference Cited List: Ref. [33] Data Tables, West Bengal, Census of India, 2011 (accessed on 13 November 2025) https://censusindia.gov.in/census.website/data/census-tables. Ref. [34] Calcutta, India Population. PopulationStat, no date (accessed on 28 August 2025) https://populationstat.com/india/calcutta.
Table 2. Studies of heat conditions in Kolkata, based on meteorological data from fixed weather stations.
Table 2. Studies of heat conditions in Kolkata, based on meteorological data from fixed weather stations.
A. Studies Reporting Single Averages for Multiple Years
ReferenceHeat MeasurementMeasurement PeriodIMD StationsTime Periods
Bal and Bal 2022
[48]		Physiologically Equivalent Temperature (PET)2-year average of monthly PET at 6 different hours of the dayAlipore, Dum Dum, Diamond Harbor2020–2021
Bal and Kirchner 2023
[49]		Physiologically Equivalent Temperature (PET)20-year average of monthly PETAlipore, Dum Dum, and 12 other IMD stations in West Bengal1986–2005
Chattopadhyay et al. 2021
[50]		Net Effective Temperature (NET), Weather Stress Index (WSI), Discomfort index (DI)10-year average of daily NET, WSI and DI at 11:30 and 17:30 h in March, April and MayAlipore2008–2017
Dash et al. 2017
[39]		Tdb, RH, HI, Humidex, Universal Thermal Climate Index (UTCI)30-year average of monthly Tdb, RH, HI, Humidex and UTCIAlipore1975–2005
Paira et al. 2023
[40]		Tdb120-year average of monthly and seasonal (summer) maximum, minimum, and mean TdbAlipore1902–2021
B. Studies Comparing Data Over Multiple Years
ReferenceHeat MeasurementMeasurement PeriodIMD StationsYears
Bal and Matzarakis 2024
[51]		Physiologically Equivalent Temperature (PET)30-year “past” average compared to 2-year “recent” average of monthly and annual PET at 11:30 and 17:30 h Alipore, Dum Dum, Canning, Diamond Harbor1979–2018, 2018–2020
Bhattacharya et al. 2010
[41]		Tdb, Tw, Thermo-hygrometric Index (THI), WBGT, Relative Strain Index (RSI)Three, 5-year averages of seasonal (March–May) conditionsDum Dum1995–1999, 2000–2004, 2005–2009
Bhattacharya et al. 2020
[43]		TdbAverage maximum and minimum seasonal (April–July) Tdb for 8 yearsBehala Airport (data from “Weather Underground”)2008, 2009, 2010, 2011, 2012, 1016, 2017, 2018
Dash et al. 2017
[39]		Tdb, RH, Wind speed, HI, HumidexMean changes in monthly averages over 30 yearsAlipore1975–2005
Debnath et al. 2023
[52]		Heat Index (HI), Environmental Stress Index (ESI)Annual maximum, minimum and average HI and ESI for 7 yearsAlipore (data from “Metroblue”) 1991, 1995, 2001, 2005, 2011, 2015, 2019
Dhorde et al. 2022
[53]		Heat Index (HI)Determination of year in which monthly (March–September) increases in HI became significantAlipore1969–2015
Gupta and Aithal 2022
[54]		HumidexNumber of days per year that Humidex > 34 °C for 10 yearsDum Dum, Behala Airport (data from “Weather Underground”)2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 1016, 2018
Jaswal et al. 2017
[55]		Heat Index (HI)Statistical analysis of HI trends over 60 years for March–May and June–SeptemberAlipore1951–2010
Kumar et al. 2022
[44]		Tdb, Humidity Index (HI), Universal Thermal Climate Index (UTCI)Number and duration of heat waves per year (Tdb > IMD threshold for maximum Tdb)Alipore1992, 1994, 1995, 1996, 2002, 2004, 2005, 2009, 2010, 2012, 2014, 2016, 2018, 2019
Neog 2024
[56]		Net Effective Temperature (NET), Thermo-hygrometric Index (THI)Number of days THI > 30 °C, and PET > 34 °C during March–September for 3 yearsProbably Alipore (Data from NASA database)2020, 2021, 2022
Neog 2024
[56]		Net Effective Temperature (NET), Thermo-hygrometric Index (THI)Annual average THI and PET for 30 yearsProbably Alipore (Data from NASA database)Yearly data between 1991 and 2022
Paira et al. 2023
[40]		TdbAnnual deviations in Tdb for 120 years relative to 120-year average AliporeYearly between 1902 and 2021
Somvanshi and Kaur 2024
[42]		Tdb, Heat Index (HI)Annual average Tdb and HI, days Tdb > 37 °C, days HI > 41 °C during “summer” (March–August) for 13 yearsNot Specified (probably Alipore)2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Somvanshi and Kaur 2024
[26]		Tdb, Heat Index (HI)Annual average Tdb and HI during “summer” (March–August) for 13 yearsNot Specified (probably Alipore)2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Tdb = dry bulb temperature, Tw = wet bulb temperature, WBGT = wet bulb globe temperature, RH = relative humidity.
Table 3. Studies of Land Surface Temperature (LST), based on satellite data.
Table 3. Studies of Land Surface Temperature (LST), based on satellite data.
ReferenceCoverageMeasurementsData SourceYears (Satellite Pass Dates)
Ali et al. 2024
[70]		KMC (Differences between boroughs, differences between land use categories)Highest and lowest LST for each borough during the year for 4 yearsLandsat-5 TM and Landsat-8 OLI data from USGS (U. S. Geological Survey)1990, 2000, 2010, 2020 (No specific satellite pass dates indicated)
Bajani & Das 2020
[73]		KMC (Differences between 6 locations)Mean LST for different areas (season unspecified)Landsat 7 and Landsat 8 data from USGS2010, 2017 (No specific satellite pass dates indicated)
Bera et al. 2021
[74]		KMC (Differences between wetlands and built areas)LST for 3 days each in March, April and May for 3 yearsLandsat-8 data from USGS 8 March 2018, 20 April 2018, 10 May 2018, 27 March 2019, 28 April 2019, 20 May 2019, 29 March 2020, 14 April 2020, 17 May 2020
Biswas & Ghosh 2021
[75]		KMA (differences between land use categories)Highest and lowest LST for each land use category during April for 3 yearsLandsat 5 TM and Landsat 8 OLI data from USGS 1995, 2010, 2020 (No specific satellite pass dates indicated)
Chatterjee & Dinda 2022
[76]		KMA (differences between land use categories)Minimum, maximum, and average LST during January and MayLandsat 5 TM and Landsat 8 TM and TIRS21 January 1999, 31 May 1999, 18 January 2009, 10 May 2009, 30 January 2019, 6 May 2019
Chatterjee & Majumdar 2022
[77]		KMC and KMA (changes in areas experiencing different LST levels) Winter LST during 1 day in 4 different years Landsat 5 TM and Landsat 8 OLI data from USGS 14 November 2000, 17 November 2005, 8 March 2019, 21 January 2010
Sen et al. 2023
[78]		KMC (differences between land use categories)LST for each land use type for 1 day in January for 3 yearsLandsat 5 and Landsat 8 OLI data from USGS24 January 2011, 6 January 2016, 3 January 2021
Dutta et al. 2022
[79]		KMC (day/night differences between land use categories)LST during day and at night for different land use types during 2 seasons in 1 yearDaytime (Landsat 8, USGS), Nighttime (Terra, MODIS from NASA)Daytime (6 May 2019, 14 December 2019), Nighttime (4 May 2019, 13 December 2019)
Gazi & Mondal 2018
[80]		KMA (differences between land use categories)Winter LST for 3 years Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI data from USGS February 2000, 2009, 2017 (No specific satellite pass dates indicated)
Ghosh et al. 2018
[81]		KMA (differences between land use categories)January LST on 1 day for 4 yearsLandsat 5 TM and Landsat 8 OLI data from USGS21 January 1991, 12 January 2001, 20 January 2011, 8 January 2017
Gupta & Aithal 2022
[54]		KMA (differences between land use categories)LST at different times of the year for 5 years. Comparison of LST with TdbLandsat 5 TM, Landsat 7 ETM and Landsat 8 OLI data from USGS11 February 2000, 25 March 2004, 7 March 2009, 21 March 2014, 6 May 2019
Halder et al. 2021
[82]		KMA (differences between land use categories)Minimum, maximum, and average LST determined for 1 day per decadeLandsat 5 TM and Landsat 8 OLI data from USGS14 November 1990, 9 November 2000, 6 February 2010, 18 December 2020
Halder et al. 2022
[83]		KMC (differences between land use categories)LST for 1 date per decade for 2 decadesLandsat 5 TM and Landsat 8 OLI data from USGS2 April 2010, 28 March 2020
Jain 2023
[84]		KMA (Nighttime conditions)Decadal changes in nighttime LST in different seasons Monthly night-time MODIS LST data from NASA2001–2010, 2011–2020
Mahata et al. 2024
[85]		KMA (seasonal differences between land use categories in Newtown area northeast of Kolkata)Seasonal LST determined for a day in each of 4 seasons every 10 yearsLandsat 5 TM, Landsat 7 ETM and Landsat 8 OLI data from USGS17 January 1991, 6 March 1991, 23 April 1991, 30 September 1991, 4 January 2001, 17 March 2001, 26 April 2001, 19 October 2001, 16 January 2011, 6 May 2011, 3 January 2021, 4 February 2021, 25 April 2021, 7 November 2021
Majumdar & Sivaramakrishnan 2020 [86]KMA (Differences in urban population)LST for 1 day in 2 decadesLandsat 717 November 2000, 21 January 2010
Majumdar et al. 2023
[87]		KMA (Differences between land use categories)LST for 1 day each decade for 5 decadesLandsat 5 TM, Landsat 7 ETM, Landsat 8 OLI data from USGS21 February 1980, 14 November 1990, 17 November 2000, 21 January 2010, 8 March 2019
Mandal et al. 2022
[88]		KMA (Differences between land use categories)LST measured every 8 days during each year MODIS product, MOD11A2 (from NASA)1 January 2001–31 December 2019
Panda et al. 2023
[89]		KMA (Differences between land use categories)Annual average, median, maximum, and minimum LSTMODIS satellite data (from NASA).March 2000–February2022
Parveen & Ilahi 2022
[90]		KMC (Differences between land use categories and boroughs within city)Minimum and maximum LST for different land use types in 2 decadesLandsat 5 TM and Landsat 8 OLI data from USGS1988, 2021 (No specific satellite pass dates indicated)
Sadhu & Satpati 2019
[91]		KMC (Differences between land use categories and different surface materials)Maximum and average LST for 1 day on different surfaces during 2 seasonsLandsat 5 TM data from USGS11 April 2010, 5 November 2010
Saha et al. 2020
[92]		KMA (Differences between land use categories and areas with different built environment densities)LST for 1 day per decade for 5 decades in different areas with different levels of built densityLandsat 5TM, Landsat 7 ETM, Landsat 8 OLI data from USGS26 December 1988, 26 January 2000, 23 December 2010, 11 January 2018
Sarkar & Sivaramakrishnan 2015
[93]		KMC (Differences between boroughs within city)LST in different boroughs with different vegetation covers for 1 dayLandsat 5 TM data from USGS8 November 2011
Somvanshi & Kaur 2024
[42]		KMC (Diurnal differences and differences between land use categoriesSummer (March–August) LST in urban and surrounding areasMODIS (Satellite data from NASA) and Landsat 7 ETM, Landsat 8 OLI data from USGS10 May 2003, 29 May 2013, 14 May 2022, 9 May 2023
KMC = Kolkata Municipal Corporation, KMA = Kolkata Metropolitan Area, Satellite pass dates noted as day/month/year.
Table 4. Changes in built areas compared to other land use forms. KMA (and environs).
Table 4. Changes in built areas compared to other land use forms. KMA (and environs).
YearBuilt Area, km2 (%)All Other Areas, km2 (%)Total, km2Reference
1980No area data (6.50%)No area data (93.50%) Majumdar et al. 2023 [87]
1988No area data (6.93%)No area data (93.07%) Saha et al. 2020 [92]
1990No area data (16.44%)No area data (83.56%) Majumdar et al. 2023 [87]
1990944.8 km2 (22.01%)3347.55 km2 (77.99%)4292.35Halder et al. 2021 [82]
1991322.68 km2 (17.16%)1557.39 km2 (82.84%)1880.07 Gosh et al. 2018 [81]
1995242.78 km2 (9.13%)2425.56 km2 (90.87%)2658.34Biswas & Ghosh 2021 [75]
2000No area data (10.37%)No area data (89.63%) Saha et al. 2020 [92]
2000No area data (25.93%)No area data (74.07%) Majumdar et al. 2023 [87]
20001349.67 km2 (31.44%)2942.68 km2 (68.56%)4292.35Halder et al. 2021 [82]
2000632.00 km2 (35.53%)1146.68 km2 (64.47%)1778.68Majumdar & Sivaramakrishnan 2020 [86]
2001502.01 km2 (27.20%)1343.42 km2 (72.80%)1845.43Ghosh et al. 2018 [81]
2010No area data (16.05%)No area data (83.95%) Saha et al. 2020 [92]
2010513.56 km2 (19.32%)2144.80 km2 (80.68%)2658.36Biswas & Ghosh 2021 [75]
2010No area data (30.17%)No area data (69.83%) Majumdar et al. 2023 [87]
20101897.58 km2 (44.21%)2394.77 km2 (55.79%)4292.35Halder et al. 2021 [82]
2010842.22 km2 (47.38%)935.54 km2 (52.62%)1777.76Majumdar & Sivaramakrishnan 2020 [86]
2011713.67 km2 (42.24%)976.04 km2 (57.76%)1689.71Ghosh et al. 2018 [81]
2017982.86 km2 (57.50%)726.44 km2 (42.50%)1709.30Ghosh et al. 2018 [81]
2018No area data (27.10%)No area data (72.90%) Saha et al. 2020 [92]
2019No area data (33.60%)No area data (66.40%) Majumdar et al. 2023 [87]
2020755.49 km2 (28.40%)1902.86 km2 (71.60%)2658.35Biswas & Ghosh 2021 [75]
20202393.75 km2 (55.77%)1898.60 km2 (44.23%)4292.45Halder et al. 2021 [82]
Table 5. Heat-related symptoms reported by elderly slum residents (n = 130).
Table 5. Heat-related symptoms reported by elderly slum residents (n = 130).
SymptomFrequency (%)
Thirst83.6
Excessive Sweating81.9
Tiredness/Weakness73.7
Muscle Cramps63.7
Dizziness60.2
Prickly Heat52.7
Headache34.5
Nausea/Vomiting17.0
Fainting5.8
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Weitz, C.A.; Mukhopadhyay, B. Evaluating the Measurement of Heat Stress in a Tropical City: Kolkata, India. Climate 2026, 14, 47. https://doi.org/10.3390/cli14020047

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Weitz CA, Mukhopadhyay B. Evaluating the Measurement of Heat Stress in a Tropical City: Kolkata, India. Climate. 2026; 14(2):47. https://doi.org/10.3390/cli14020047

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Weitz, C. A., & Mukhopadhyay, B. (2026). Evaluating the Measurement of Heat Stress in a Tropical City: Kolkata, India. Climate, 14(2), 47. https://doi.org/10.3390/cli14020047

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