The relationship between the urban heat island effect and allergic diseases in young children

Abstract

Background:

The urban heat island (UHI) significantly affects the health of people living under its influence. Urban children are highly exposed to environmental exposures that can affect the occurrence of atopic diseases. This study aimed to determine the link between air pollution, the UHI phenomenon, and allergic diseases in preschoolers.

Methods:

The study group included 310 5-year-old children attending kindergartens in the urban and rural areas. Each child had skin prick testing (SPT), the measurement of volatile organic compounds (VOCs) in EB, and measurement of particulate matter (PM2.5 and PM10) using the personal aspirators. The children’s health questionnaire covered the following symptoms: asthma, food allergy, allergic rhinitis (AR), and atopic dermatitis (AD). The analysis of the UHI phenomenon was based on data from the Weather Research and Forecasting (WRF) model for the years 2014–2019. Air quality analysis (PM10 and PM2.5) was performed for the years 2015–2019 using the CALMET/CALPUFF modeling system.

Results:

The place of residence of 165 assessed children was localized in a rural area, whereas 145 children were lived in an urban area. A total of 44 children lived in the UHI zone: 71.8% of them lived in tenement houses with traces of moisture and mold at home (35.9 and 33.3%, respectively), and 69.2% of them were exposed to tobacco smoke. Children from the UHI often significantly suffered from atopic dermatitis (25.6%) and allergic rhinitis (28.2%). The highest concentration of PM2.5 and PM10 was confirmed in children living in the UHI. There was a clear trend in the VOC profile between the three categories of place of residence (rural area, urban area, and urban heat island).

Conclusion:

We demonstrated that living in the UHI zone independently increased the risk of developing atopic dermatitis and allergic rhinitis in our cohort.

Introduction

Allergy has become a serious public health concern since its prevalence has been increasing in the last two decades, affecting 30–40% of the world population (Sánchez-Borges et al., 2018). Air quality and other environmental factors are gaining importance in public health policies since it is known that they can affect the occurrence of atopic diseases and asthma in the first years of life (Asher et al., 2010; Nishimura et al., 2013). Urban children are exposed disproportionately to high levels of air pollution and other environmental exposures (Canaday et al., 2024; Olaniyan et al., 2020). Every year, over 1,200 deaths in people under 18 years of age are estimated to be caused by air pollution in European Environment Agency (EEA) member and collaborating countries (European Environment Agency, 2026). Particulate matter (PM2.5 and PM10) is a suspended dust with a diameter of <2.5 or <10 μm that is produced during combustion, traffic, and industrial activities (World Health Organization, 2016). It can irritate the nasal and bronchial mucosa, causing the release of inflammatory mediators. This can lead to increased pulmonary inflammation and aggravate the symptoms of atopic diseases among urban children. Exposure above the WHO air quality guidelines 2021 (AQG) value for PM10 was noticed in 96% of the population, for PM2.5 in 83%, and for O₃ in 94% (European Environment Agency, 2025). This is particularly dangerous for patients with asthma, as exposure to PM can lead to acute exacerbation of their ailment, which can prove fatal (European Environment Agency, 2026).

Researchers are interested in not only environmental pollution but also urban heat islands (UHIs), a well-studied phenomena in which metropolitan areas experience higher temperatures than their surrounding suburban and rural areas due to human activities (Bärring et al., 1985; Landsberg, 2026). In fact, the UHI significantly affects the health of people living under its influence. Urbanized UHI areas are characterized by elevated ambient temperatures, increased concentrations of carbon monoxide, carbon dioxide, sulfur dioxide, and nitrogen dioxide, as well as increased concentrations of PM and ozone, causing an increase in allergen production. Those factors can alter plant physiology and increase allergen production (Choi et al., 2021; Pałczyński et al., 2018), which can have an indirect effect on the exacerbation of symptoms of respiratory allergic diseases by extending the pollen period and increasing the allergenicity of pollen (Choi et al., 2021; Pałczyński et al., 2018).

Profiling of volatile organic compounds (VOCs) in exhaled breath is a promising method in asthma diagnosis, monitoring, and treatment (Neerincx et al., 2017; Shahbazi Khamas et al., 2024), especially in young children, in whom the diagnosis of asthma is difficult due to the inability to perform spirometry (Yang et al., 2019). Endogenous VOCs are produced during metabolic processes of the human body and their settled microorganisms and can be used as biomarkers (Horváth et al., 2017). One or more individual VOCs or particular composite patterns of VOCs may be assessed in connection to the presence of the type of inflammation in airways, e.g., an atopic and non-atopic asthma (Fens et al., 2013; van de Kant et al., 2012). In this study, an assessment of VOC profiles significantly improved the diagnostic process of asthma in preschool-age children with preclinical asthma and separated them from patients with recurrent but transient wheezing. (Fens et al., 2013). Therefore, it is of interest to evaluate the VOCs in exhaled breath in children with regard to the presence of asthma and the environmental condition around their residence.

Lodz is located in the central part of Poland, and it forms with the cities directly adjacent to an agglomeration with a population of almost 1 million. Our previous research has confirmed the existence of the UHI in Lodz (Bobrowska-Korzeniowska et al., 2024). Furthermore, the prevalence of allergic diseases in our city is high. According to epidemiological studies, almost 40% of urban population may suffer from allergic rhinitis and approximately 20% from asthma (Samoliński et al., 2014). Therefore, it is important to assess the relationship of the UHI with the occurrence of various allergic diseases.

This study aimed to determine the possible link between air pollution, the urban heat island (UHI) phenomenon, and allergic diseases in preschoolers. This study attempted to address the following problems: (1) to compare the clinical and socioeconomic data of children by place of living—rural and urban area; (2) to compare the individual exposure to PM2.5 and PM10 by place of living; and (3) to find the relationship between the urban heat island effect and VOCs and allergic diseases.

Methods

The study group consisted of 310 5-year-old children attending 12 randomly selected kindergartens in the urban (city of Lodz) and rural (Lodz Voivodeship) areas (six kindergartens in each area). To be included into the study, children had to have lived in the area continuously since their birthday. After receiving permission from headmasters of the kindergartens, the aim and procedures of the study were explained to parents/caregivers by the research team, consents for children’s participation in the study were obtained, and the questionnaires filled out by the parents/caregivers were collected. Next, a medical doctor and a researcher from the Lodz Regional Park of Science and Technology visited each kindergarten and performed the study procedures. At this visit, each child underwent skin prick testing (SPT) to assess the allergy, the measurement of VOCs in exhaled breath was performed to assess asthma, and individual backpacks containing GilAir Plus Basic personal aspirators for 24 h measurement of air pollution (PM 2.5 and PM 10) were distributed. After 24 h, the backpacks with the equipment were collected from children by the study staff. The complete description of the methodological assumption has been published elsewhere (Bobrowska-Korzeniowska et al., 2021). The study was approved by the Ethics Committee of the Medical University of Lodz (decision No. RNN/390/17/KE). All obtained data were used only for research purposes.

Questionnaire

The questionnaire completed by the parents/caregivers of the child included data on the child’s socioeconomic data, living conditions, and air pollution exposure. The second part of the questionnaire contained questions regarding the child’s health, including doctor-diagnosed allergy, asthma, atopic dermatitis, and food allergy; the severity and frequency of diseases; hospitalizations; and medications taken (ISAAC, 2013). The survey was developed based on the International Study of Asthma and Allergies in Childhood and the European Community Respiratory Health Survey and was used and published by the study team previously (ISAAC, 2013; Bobrowska-Korzeniowska et al., 2022).

Skin prick testing

Skin prick testing was performed with the most common inhalant allergens: house dust mites, birch, hazel, alder, mixed grass pollen, rye, ribwort, mugwort, Alternaria, Cladosporium, dog and cat dander, and a negative (glycerol) and positive (histamine chloride 10 mg/mL) control (manufactured by HAL Allergy, Leiden Bio Science Park, the Netherlands). A positive SPT reaction was defined as a mean weal diameter of >3 mm in excess of the negative control, according to the European Academy of Allergy and Clinical Immunology (Burbach et al., 2009; Heinzerling et al., 2013).

Assessment of volatile organic compounds

In our previous study, we described two VOC clusters associated with asthma and atopic dermatitis (Bobrowska-Korzeniowska et al., 2022). In this study, we used the same cohort of children to include this variable to assess the relationship between atopic diseases and environmental factors. Below, we briefly present the methods used for the isolation and analysis of VOCs.

VOCs were measured in each child’s exhaled breath using the gas chromatography–mass spectrometry (GC–MS) method in a laboratory located in the Lodz Regional Park of Science and Technology. Breath samples were collected in Tedlar^® bags and then transferred to the Tenax TA-filled tubes (GERSTEL GmbH & Co. KG, Mülheim an der Ruhr, Germany) using a peristaltic pump. After that, the volatiles were desorbed in the thermal desorption unit (GERSTEL GmbH & Co. KG, Mülheim an der Ruhr, Germany). Mass spectra were collected using a Pegasus 4D Time-of-Flight Mass Spectrometer (Leco Corp., St. Joseph, MI, United States). The settings of the spectrometer were as follows: an ion source temperature of 200 °C, an ionization energy of 70 eV, and a scan range of 33–450 atomic mass units at 50 spectra/s. The obtained chromatograms were processed using the Statistical Compare feature implemented in the ChromaTOF software (LECO Corp., St. Joseph, MI, United States). Peaks were aligned across all samples based on retention time and mass spectral similarity. Only peaks with a signal-to-noise ratio (S/N) greater than 200 were retained for further analysis. Tentative compound identification was performed by comparison with the NIST/EPA/NIH and Wiley mass spectral libraries, and only matches with similarity scores above 80% were accepted. Additionally, retention indices (RIs) were calculated using a standard n-alkane series analyzed under identical chromatographic conditions, and the obtained RI values were compared with literature data to confirm compound identity. All compounds meeting these criteria, even if detected in a single sample, were included in the dataset for subsequent comparison between the study groups. This approach ensured that both common and rare VOCs were reliably identified and that the final dataset reflected all analytically and biologically relevant features.

Environmental assessment (air pollution)

Air quality analysis for the years 2014–2018 (the period from the birthday year of children to the year of participation in the study) was performed using the CALMET/CALPUFF modeling system developed in Earth Tech, Inc. in California. In selected locations in the rural and urban areas, Łódź mathematical modeling of the spatial distribution of concentrations of PM10 and PM2.5 was performed. The software code written in Fortran is available at http://www.src.com/calpuff/ download/download.htm. It is fully compilable, which allows for its customization to a specific case. Meteorological parameters for modeling dispersion of pollutants in the Łódź region were provided by the numerical weather prediction system Weather Research and Forecasting (WRF). The input sources for the model were the University Corporation for Atmospheric Research (UCAR) and the National Center for Atmospheric Research (NCAR) (NCAR Research Data Archive).¹ The emission information was delivered to the model in the form of a cadastre (grid) part of the CALMET/CALPUFF modeling system. The CALMET preprocessor was responsible for the preparation of the original field information and meteorological data for the CALPUFF model’s input. Meteorological calculations were performed in a regular grid (grid), including areas with emissions. For the purpose of this study, detailed field information was created in appropriate resolutions.

PM concentrations were calculated at resolutions as follows: 250 m in rural areas (the residence of village participants), 500 m in Lodz, and 1,000 m in other municipalities within the calculation grid. In addition to air pollution modeling for each participant, individual air pollution measurement was performed using GilAir Plus Basic personal aspirators (Clearwater, FL, United States). The GilAir Plus Basic personal sampling pump measuring dust, gasses, and vapors has the capability of generating and controlling flow over the range of 20–5,000 cc/min in two flow ranges, 20–449 cc/min and 450–5,000 cc/min, which are selectable using a 2-mm or 5/64-inch hex key (provided with the pump). The air flow rate used for sampling was 3.5 L/min. The PM2.5 and PM10 aspirators were placed at home for 24 h. The collected dust samples were analyzed in the Aerosol Laboratory of the Nofer Institute of Occupational Medicine in Lodz.

Urban heat island analysis

The analysis of the UHI phenomenon was based on data from the Weather Research and Forecasting (WRF) model for the years 2014–2019 and modeled at household-level resolution (NCAR, 2018). Calculations were performed using the WRF model in two nested grids, the first with a resolution of 15 km covering a large part of Europe and the second with a resolution of 5 km covering the area of Poland with a margin of approximately 250 km. To refine, data from the WRF model were processed using the CALMET preprocessor to 1 km resolution (Scire et al., 2000). The UHI index and the contrast criterion were used as basic indicators determining the urban heat island phenomenon.

The UHI index was defined as the difference of at least 1.5 °C in the daily minimum temperature between a point representing rural conditions (lower temperature) and a point located in strictly urban conditions (higher temperature), such as the city center (Błażejczyk, 2026; Błażejczyk et al., 2014).

The UHI contrast criterion was defined as the occurrence of a temperature difference of at least 1.5 °C for a single hour in the day between the warmest point in the urban area and a selected point in the rural area. This criterion allows the determination of UHI instances (Błażejczyk, 2026; Błażejczyk et al., 2014).

Based on data from the WRF/CALMET model and the above measures, the occurrence of UHI cases in Lodz in 2014–2019 was analyzed. For the analysis, five single grids from CALMET meteorological data were chosen. Each grid represents virtual stations with different thermal characteristics: one station in the dense residential area in the city center, two stations representing rural conditions, and two also representing rural conditions outside the borders of Lodz. For those grids, hourly temperature values were plotted, and the above-mentioned indicators were then calculated.

The complete description of the methodological assumption and the results of the analysis of the UHI phenomenon in the city of Lodz in 2014–2019 have been published elsewhere (Bobrowska-Korzeniowska et al., 2024).

Statistical analysis

Sample size was calculated as follows: A 3-group design was used to test whether there is an increasing or decreasing linear trend in proportions. The hypothesis was evaluated using a two-sided Cochran–Armitage Z-test with continuity correction, with a Type I error rate (α) of 0.05. To detect a proportion sequence of 0.1, 0.2, and 0.3, with 90% power, the total sample size needed was at least 300 (with the proportion between groups: 1:0.7:0.3).

Between-group comparisons were followed at baseline using Fisher’s exact test, the chi-square test for trend, the Cochran–Armitage test for trend in proportions, the Mann–Whitney test, or the Kruskal–Wallis test. The main part of the analysis was carried out in two stages. First, dependent variables were defined. Next, a logistic regression analysis was used to define variables independently associated with the UHI. A logistic regression analysis was performed in the univariate model followed by the multivariate model. The multivariate model was built according to the step-forward selection approach; coefficients associated with the dependent variable in the univariate model with a P-level of < 0.1 were only included. A p-value of less than 0.05 was assumed to be statistically significant. Statistica 13.1 (TIBCO Software Inc.) was used to perform all analyses.

Results

Data were collected from all children participating in the study.

Characteristics of children from the urban heat island, urban area, and rural area

Place of residence (continuously since birthday) of 165 assessed children was localized in a rural area, whereas 145 children lived in the city of Lodz (urban area). Of 145 children living in an urban area, 44 children lived in the urban heat island (UHI) zone of Lodz. Socioeconomic data describing the place of residence and allergic morbidity were compared between groups of children living in the urban heat island and children from another place of residence—rural and urban areas outside the UHI (Table 1). It was shown that 69.2% of children living in the urban heat island were exposed to tobacco smoke; 71.8% of them lived in tenement houses with traces of moisture and mold at home (35.9 and 33.3%, respectively). The level of education of parents in the UHI was significantly lower: 28.2% of mothers completed primary school, 21.9% of fathers completed primary school, and 34.4% of them completed vocational school. Almost 95% of parents in the UHI reported average economic status, and 2.6% of them reported low economic status.

The relationship between the place of residence—UHI, urban area, and rural area—and outdoor pollution

Residential air pollution was assessed over a 5-year period (over the whole life of study participants) using the CALMET/CALPUFF modeling system, and a single measurement of indoor pollution was made by personal dust aspirators in each home. A clear relationship was demonstrated between outdoor and indoor pollution assessed by PM2.5 and PM10 parameters and the place of residence. The highest concentration of PM2.5 and PM10, assessed individually and calculated from the average annual local exposure, was confirmed in children living in the UHI (Table 2).

We observed moderate linear correlations between annual average concentrations of PM and PM measured by personal aspirators only in the urban and UHI areas (Supplementary Table 1).

For a cumulative assessment of long-term exposure to PM (separately for PM2.5 and PM10), mean annual concentrations in the subsequent observation years 2014–2018 were summed for each study participant and defined as total PM exposure in 2014–2018. Next, a receiver operating characteristic (ROC) curve analysis was performed to identify PM and its cut-off point for predicting atopic diseases. Based on the Youden index, total PM2.5 exposure in 2014–2018 was found to be most strongly associated with the occurrence of atopic dermatitis (cut-off point > 114), food allergy (cut-off point > 118), allergic rhinitis (cut-off point > 114), and asthma (cut-off point > 120) (Supplementary Figures 1, 2).

The relationship between the place of residence—UHI, urban area, and rural area—and the VOC profile

The relationship between indoor conditions specific to the UHI and allergic morbidity and the VOC profile was analyzed. Clustering of exhaled VOCs revealed two clinically distinct groups of children: Cluster 1, atopic dermatitis was more frequent in this group of patients, and Cluster 2, children more frequently suffered from asthma, were characterized by specific VOCs defined in the previous study (Bobrowska-Korzeniowska et al., 2022).

There was also a clear trend in the VOC profile between the three categories of place of residence (Table 3). Cluster 1 was observed in 97.1% of children from the UHI zone, in 89.5% of children living in a city outside the UHI zone, and in 55.6% of children living in the rural zone. Cluster 2 was observed in 2.9% of children from the UHI zone, in 10.5% of children living in a city outside the UHI zone, and in 44.4% of children living in the rural zone.

The relationship between the place of residence—UHI, urban area, and rural area—and the occurrence of allergic diseases

There was a clear trend in the prevalence of atopic dermatitis (AD) and allergic rhinitis (AR) among the three categories of place of residence (Table 3). Children from the UHI zone often significantly suffered from atopic dermatitis (25.6%) and allergic rhinitis (28.2%). There was no statistical significance in the prevalence of asthma and food allergy between the groups. Visualization of the above phenomenon, together with characterization of the population of children living in the UHI, is presented in Figure 1.

Figure 1

Risk factors of allergic diseases

In our cohort, we observed differences between regions of residence in exposure to factors that may increase the risk of atopic diseases. Therefore, all of the above factors, along with the level of exposure to PM and the place of residence, were included in a logistic regression analysis to identify the strongest and independent predictors of atopic diseases (Table 4).

Multivariate analysis revealed that only the place of residence was associated with atopic dermatitis. Increased exposure to particulate matter was associated with food allergy. Place of residence and visible window moisture were associated with allergic rhinitis. Cluster 2 emerged as the only independent predictor of asthma (Table 4).

In our cohort, we demonstrated that living in the UHI zone independently increased the risk of developing atopic dermatitis and allergic rhinitis.

Discussion

Our study highlights the relationships between environmental factors such as urban heat islands (UHIs) and air pollution, living conditions, socioeconomic status, and allergy diseases in preschoolers. We showed that children from the UHI zone were more often diagnosed with allergic rhinitis and atopic dermatitis, the family economic situation was worse, parents had a lower education level and smoked at home, and there were signs of moisture and molds at the place of living.

The highest concentration of PM2.5 and PM10, assessed individually and calculated from the average annual local exposure, was confirmed in children living in the UHI zone. Associations between allergy and air pollution are supported by existing literature (Asher et al., 2010; Chen et al., 2022; Nishimura et al., 2013; Zhang et al., 2023). A meta-analysis conducted by Wang et al. has revealed that exposure to air pollutants might increase the risk of immunoglobulin E (IgE)-mediated allergic diseases. Long-term exposure to PM2.5 was associated with the development of allergic rhinitis and PM10 with eczema (Wang et al., 2022). Mohd Isa et al. have found a significant association between exposure to PM10 and skin allergy symptoms in the past 12 months in teenagers in Malaysia (Mohd Isa et al., 2020). Data from the PIAMA birth cohort revealed that exposure to air pollution such as PM2.5 at the place of residence early in life may be associated with an increased risk of developing asthma through childhood into early adulthood (Gehring et al., 2020). However, in our study, no statistically significant difference was found in asthma between the groups, diagnosed by doctors and measured by VOCs. Perhaps, a follow-up would be needed since it is known that the age at diagnosis of asthma increased over calendar time (Engelkes et al., 2015).

The highest prevalence of atopic dermatitis (AD) and allergic rhinitis (AR) in our study was observed in children from the UHI zone, which is consistent with previously published data (Hui-Beckman et al., 2023). Hui-Beckman et al. have revealed that repeated exposure to temperature fluctuations causes persistent skin barrier dysfunction and can lead to increased allergic sensitization (Hui-Beckman et al., 2023). According to Kam et al., climate-associated phenomena may enhance the environmental triggers of atopic dermatitis and are associated with mental health comorbidities, including anxiety and depression (Kam et al., 2023). Less residential green space may intensify the association between air pollution exposure and infantile AD (Lee et al., 2018). Assessment of more than 39,000 Chinese preschool children revealed an association between multisource anthropogenic heat (AH) exposure (building heat, industrial heat, metabolic heat, transportation heat, and total heat) and the risks of respiratory allergy symptoms; for example, exposure to higher levels of building heat, transportation heat, and total heat was associated with increased risks of wheezing, and exposure to building heat, transportation heat, metabolic heat, and total heat was associated with symptoms of rhinitis (Niu et al., 2025). Climate warming can influence the severity of the pollen season by changing the amount, timing, and distribution of pollens, which may have important health impacts (Zhang and Steiner, 2022; Ziska et al., 2019). Rising temperatures and increased levels of CO2 enhance plant growth and extend the length of growing seasons (Zhao et al., 2025). Climate change can also alter the timing of pollen seasons (Ziska et al., 2019). Warmer temperatures cause plants to pollinate earlier in the year, which can extend the duration of pollen seasons (D'Amato et al., 2023; Zhang and Steiner, 2022; Zhao et al., 2025). These dynamics seem to be observed in UHI zones (Hui et al., 2025), which is confirmed by our study. Children are vulnerable to the negative effects of breathing in polluted environments in the air because of different breathing patterns and higher respiratory rates. They inhale a volume of air containing a greater amount of pollen per body weight than adults (Gleason et al., 2014).

Data from the Japan Environment and Children’s Study showed that low household income was a risk for eczema in small children, and lower economic status was noticed in the UHI zone group in our study (Kojima et al., 2022). The Greek part of the GAN Phase I showed that adolescents with caregivers having low education are more likely to have symptoms of eczema and rhinitis, which is consistent with our observation (Antonogeorgos et al., 2022). In our study, cluster 2 of exhaled VOCs was more prevalent in children living in the rural area in which asthma was more often diagnosed. Other investigations also indicate that asthma morbidity in the agricultural settings is at least as high or even higher than in urban communities (Malik et al., 2012; Pesek et al., 2010).

Our findings support a literature that indicates that many different environmental factors may have detrimental effects on the respiratory health of rural residents (Luedders and Poole, 2022). Despite the differences in PM composition, the exactly higher proportion of organic dust in PM from rural locations, the ability to trigger inflammation, and the ability to cause adverse health effects seem to be similar.

The strength of our study is that the assessment of individual-level exposure is based on estimation from models (for the years 2014–2018, the period from the birthday year of children to the year of participation in the study) and also on individual personal monitoring, allowing for the assessment of indoor exposure. We decided to use personal aspirators, which sampled mainly indoor dust, bearing in mind suggestions from previous epidemiological studies that every attempt at the assessment of indoor and personal concentrations in life environments is necessary to evaluate the total exposure to air pollution (Bo et al., 2017). Improved personal monitoring that enables individuals to assess their own exposure is recommended by EAACI and may allow tailored interventions (Agache et al., 2025). Some investigators have suggested that indoor exposures may be treated increasingly as an individual’s total exposure since time activity studies have revealed that children can spend as much as 90% of their time indoors (Breysse et al., 2010).

The limitations of our study are a small sample size and only a single measurement of environmental pollution (PM2.5 and PM10) over 24 h using personal aspirators was performed.

It is important to consider when assessing the phenomenon of the UHI that residences localized near urban green infrastructure (e.g., small parks) are assigned a high UHI grid value, overestimating their true heat exposure, whereas residences in local hotspots (e.g., asphalt parking lots) are assigned an averaged, lower grid value, underestimating their true heat exposure (Chen et al., 2026; Nagar et al., 2025). Although the results of modeling are much less sensitive than the results of measurements, we could clearly determine the days with the occurrence of the urban heat island phenomenon (Bobrowska-Korzeniowska et al., 2024).

It is noteworthy that the analysis carried out in our study covers 6 years (2014–2018)—the whole life of our participants—and residential mobility was an exclusion criterion. The attempt to verify the independence of the effect of the place of residence (heat island phenomenon) and the characteristics of the influence on the risk of developing atopic diseases in the multivariate regression model was significantly limited by the relatively small sample size.

The results of a previously published paper indicate a consistent and significant statistical association between lower socioeconomic and minority status and greater urban heat risk in US cities, which has been described as a “landscape of thermal inequity” (Mitchell and Chakraborty, 2015). We showed that a similar landscape may exist in Lodz, and children living in the urban heat island are exposed to tobacco smoke and moisture at home and live in tenement houses with traces of molds. The parents of these children have a lower level of education and a worse economic situation. However, consideration should be given to whether UHI is the causal factor or if it is a proxy for deprivation, since it is known that low-income areas face 2–3 °C higher UHI exposure (Yuan et al., 2026).

Our data reflect an association among living conditions, air pollution, and UHI, but cause-and-effect relationships are still not clear. Nevertheless, we may suggest that public health specialists should provide care to groups susceptible to these conditions.

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