How the Chicago region is affected by the Urban Heat Island

How the Chicago region is affected by the Urban Heat Island

This blog is adapted from my master’s research in using GIS to measure the urban heat island effect across the Chicago region. This interest comes from the fact that as someone who has commuted on many hot nights in/around Chicago, there is an obvious different in how the urban heat island impacts the urban, impervious core, and the tree canopied areas in the more suburban parts of our region. I hope that this understanding of how the urban heat island disproportionately affects the urban poor, elderly and disabled with an already enhanced risk of negative health effects leads to mitigation strategies, like cooling roofs and planting trees, to reduce the future urban heat island impacts, especially in the face of a warming planet.

The Urban Heat Island (UHI) phenomenon, where the urban core of an area becomes warmer than the surrounding region, has become a critical concern for urban areas due to its adverse impacts on human health, energy consumption, and overall urban sustainability. This impact is further exacerbated by ongoing anthropogenic climate change, and increased suburbanization. However, the impacts of the urban heat island are not uniform, and are influenced by local factors such as tree canopy, waste heat, land use, land cover, and urbanization (Ackerman 1985).

There have been numerous approaches to measuring, mapping, and quantifying the urban heat island using various geographic information systems (GIS), such as spatiotemporal analysis using temperature readings, remote sensing, and machine learning algorithms, among other approaches (Jato-Espino et al. 2022). Using these approaches at different scales has increased our understanding of the main drivers of the urban heat island, such as land use, land cover, tree canopy, waste heat, and urbanization, as well as what we can do to mitigate them (Stone 2012).

Each of these factors can be mapped and analyzed using GIS tools. This project focuses on the City of Chicago and the surrounding region, which despite its unique geography on the shore of Lake Michigan, has been prone to heat wave amplification because of the urban heat island effect. The most consequential outcome occurred during the summer of 1995, when a heat wave was a driving factor in hundreds of deaths within the City of Chicago and Cook County (Klinenburg 2003). Mitigating the urban heat island effect will hopefully save lives when major heat waves occur.

The effects from the 1995 heat wave highlighted the urgent need for proactive measures to address heat-related risks. Having learned painful lessons from the heat wave, the question for public health, urban planning and other professionals involved in handling major heat waves is not whether another major heat wave will occur, but simply a matter of when the next heat wave will occur.

Using GIS and conducting analyses of which weather stations read the highest temperatures relative to other stations, one can map where places are most impacted by, or most at risk from, urban heat island effects. These insights can help professionals and policymakers to prioritize targeted interventions and implement effective mitigation strategies, such as increased tree canopies and green roof systems, among other approaches.

Understanding where these locations are, and why they are warmer than other parts of the city can also show where the potential for the greatest impact may be in deploying resources and opening cooling centers during major heat waves. However, it is not just temperatures that must be analyzed, but the populations of the city as well. For example, during the 1995 heat wave, the City of Chicago had numerous resources for residents to use to cool off from the heat; but many of the victims were elderly, and lacked transportation or social ties that could get them to these facilities, which inhibited the potential to save lives (Klinenburg 2003).

Furthermore, understanding the spatial patterns of the urban heat island effect at a micro-level within the city can help identify these vulnerable communities and populations that may experience associated health risks with heat waves. Low-income neighborhoods, areas with limited access to green spaces, and densely built urban cores are often more susceptible to the urban heat island effect (Coseo and Larsen 2015).

Unfortunately, many of the most vulnerable populations in the City of Chicago also live in the areas that are most impacted by the urban heat island (Coseo and Larsen 2014).
By being aware of where the most vulnerable communities are, and where the warmest temperature readings are most likely to occur, the goal is ensure a just and resilient urban environment for all residents. In a warming climate, mitigating the urban heat island will be essential in protecting human lives and overall public health.

The objective of this research is to analyze the spatial patterns of the urban heat island effect within the Chicagoland region and compare weather station readings during the warmest nights in the region. This is to analyze the urban heat island effect on different parts of the region as it relates to warm minimum temperatures, which historically has not been studied as much as the extreme heat occurring during the day. However, the presence of very high minimum temperatures during heat waves can prolong one’s exposure to extreme heat and amplify negative health outcomes.

Urban Heat Island Characteristics

Several studies have examined the UHI characteristics in Chicago, shedding light on its spatial and temporal variations. Lai and Dzombak (2019) noted that one of the difficulties in studying the Urban Heat Island effect is the fact that historical data has station location changes, site characteristic changes, increased urbanization, and instrumentation changes over time, during their observation of the United States Historical Climatology Network Daily Temperature (Menne et al. 2015).

Despite the difficulty in effectively homogenizing data for these changes, Ackerman (1985) conducted a comprehensive analysis of the Chicagoland region’s urban heat island effect, providing insights into the evolution of UHI over time. The data was collected from Midway Airport and Argonne National Laboratory to compare the seasonal variations of the UHI effect, which Ackerman found are more pronounced in the summer and winter, with April and September being the months of least prevalence.

Further studies have confirmed this, except for the presence of the lake breeze effect which Sharma (2016) found to moderate the temperature in downtown Chicago near Lake Michigan. Nonetheless, the presence of Lake Michigan makes it more difficult to fully measure the urban heat island effect within Chicago as there are many local microclimates at play.

Alessandro et al. (2022) studied these microclimates, by using a network of sensors across the city to measure subsurface impacts to UHI in subsurface streets, parking lots, basements, etc. The authors found a tendency for waste heat and daytime warmth to get trapped in underground areas and those without much exposure to the mixing of air. The researchers further found that temperature amplitudes in subsurface streets are much higher than above ground, noting the lack of variation of “synoptic events”, i.e., weather related air mixing through wind, rain, etc.

The effect of Urban Heat Islands on heat wave intensity is another research focus since these effects can exacerbate already intense heat.  Basara et al. (2010) utilized two datasets collected from 15 July through 15 August 2008, which spanned the period prior to, during, and following the heat wave event in Oklahoma City, and found that throughout the study period there was a consistent UHI of about 0.5 degrees C during the day and 2 degrees C at night within the urban core, after observing 46 locations in and around Oklahoma City. To put it simply; the most urban areas around Oklahoma City were 0.5 to 2 degrees C warmer than more outlying areas. While that may not sound like a large difference, in an already hot weather spell, this can increase the heat index, and push already uncomfortable temperatures into dangerous territory. As the effect was more pronounced at night, the effect inhibits relief from the daytime extreme heat, furthering the length of time people are exposed to extreme heat.

Alfraihat et al. (2016) carried out an ecological evaluation of the UHI in Chicago, examining its extent and impacts on the urban environment, which confirmed the Ackerman (1985) analysis, which found that UHI effects were more pronounced in Chicago’s most extreme seasons, which are summer and winter. The UHI effects were also positively correlated with land use and land cover, even in Chicago with the lake breeze effect. In the context of Ackerman’s research, the authors’ research also examined more thoroughly the effects of UHI on air quality, human health and energy consumption.

Within these studies, the findings appear to be consistent with one another, although there is significant variation in the approaches and observations of the authors based on the level of complexity of their research, which makes spatial analysis of UHI difficult to correctly map, as numerous land uses, land covers, both anthropogenic and natural effects, and building types influence the UHI effect.

As technology and infrastructure has allowed more hyperlocal analysis of the UHI, our knowledge on its drivers has grown, and one can see this in play when comparing the above research strategies; Ackerman (1985) only had two data locations (Argonne and Midway); Basara et al. (2010) had 46 different locations to study the Oklahoma City 2008 heat wave event, and Alessandro et al. (2022) were able to use a network of hundreds of sensors across Chicago to find hyperlocal temperature variations.

Urban Heat Island Hazards

Understanding the hazards associated with UHI is crucial for developing effective mitigation strategies. Klinenberg (2003) conducted a social autopsy of the 1995 Chicago heatwave, which found numerous systemic issues with the response to the 1995 heat wave, which led to numerous unnecessary negative health outcomes. As one example, although Chicago had cooling centers for residents without air conditioning, many were unaware of their existence, and many more still were unable to transport themselves to one. Such issues will need to be addressed in the future to build more resilience on a changing planet.

Although Klinenberg used a sociological approach with his analysis, his research is backed by meteorological science, such as Changnon et al. (1996), who concluded that an increase in the number and intensity of heat wave events is likely to occur in a warmer environment, and so it is necessary to plan for these and develop mitigation strategies to prevent negative health outcomes.

Chen et al. (2022) also conducted a case study on the 2012 Chicago heatwave, estimating heat-related exposures and the impacts of the UHI. The 2012 heat wave involved a much drier air mass, and although the 2012 event had similar temperatures to the 1995 event, it was far less deadly by comparison, because of both lower heat indices and painful lessons learned from 1995.

Using data and insights gained from the 1995 event, Sailor (2014) further on these findings from the Chicago heat wave and other events by evaluating the risks of UHI from climate change itself, as well as how increased urbanization and suburbanization proliferates the UHI effect.

Urban Heat Island Mitigation

To mitigate the UHI effect, various strategies and interventions have been proposed and implemented. Coseo and Larsen (2014) built upon the research of Stone (2012), who identified four principal characteristics that make cities hotter than surrounding areas. These characteristics were (1) the reduction in evaporative cooling, (2) low surface reflectivity, (3) vertical surfaces, and (4) waste heat, and suggested that these variables are more carefully analyzed in future UHI research. To briefly discuss why these characteristics are important for mitigating UHI, when water evaporates, it reduces the air temperature as it draws in heat from its surroundings. Surface reflectivity refers to the Albedo effect, which reflects sunlight back to space; the lower the albedo, the more solar radiation is absorbed, increasing heat. Vertical surfaces absorb more heat than horizontal surfaces, and usually have a lower natural albedo, such as roofs and buildings. Waste heat is the byproduct of these buildings, as well as impervious surfaces such as roads and sidewalks.

Coseo and Larsen (2014) selected eight Chicago neighborhoods emphasizing different socioeconomic, racial, and land use patterns to attempt to find differences in UHI. The authors also noted that the UHI impact changed between neighborhoods not just during seasons, but between day and night cycles; the warmest neighborhood during the day was different than the warmest neighborhood at night. By comparing the UHI effect on different neighborhoods, the author’s research can be used to make better informed planning decisions which can help mitigate the UHI effect, such as creating more tree canopies and using more reflective roofing material.

Alessandro et al. (2022) also built upon the Stone (2012) study by observing two UHI characteristics; vertical surfaces, and waste heat. Sharma et al. (2016) examined the potential of green and cool roofs in mitigating the UHI effects in the Chicago metropolitan area. The authors’ concluded that daytime and nighttime temperatures responded differently between cool roofs and green roofs, which also makes sense, as vegetation may soak in warm air during the day, but also store that heat at night, reducing its mitigation impact.

Part of mitigation strategy involves maintaining the green infrastructure created as a response to climate change, both within the context of UHI and beyond. Reynolds et al. (2020) assessed how climate change will impact urban forests, gardens, and green infrastructure in neighboring Indiana, and their resilience to impacts such as water stress, pests, disease, invasive species, flooding, frost, and timing of maintenance.

As Indiana faces many of the challenges that Chicago does, both in the adjacent developed Lake County, IN as well as Indianapolis, these strategies apply to Chicago and many other Midwestern cities and beyond. Further research into how to maintain these infrastructure developments will get the most out of them and serve as best practices as part of a holistic approach to heat resilience.

Urban Heat Island GIS/Data

GIS and data analysis play a crucial role in understanding the UHI effect and developing appropriate strategies. Several studies have explored the use of GIS and data-driven approaches in studying the UHI phenomenon. Li et al. (2018) developed a high-resolution daily air temperature dataset for urban areas in the United States, enabling more accurate UHI assessments. Jato-Espino et al. (2022) introduced ArcUHI, a GIS add-in that uses machine learning to model the Surface Urban Heat Island (SUHI) effect, which is the difference in temperature between urban and rural areas, using open access datasets and processes from ArcGIS’ tools. The tool was tested using data from Madrid, meaning it is possible that certain microclimates Chicago possesses (such as Lake Michigan) may yield different results.

Lai et al. (2018) explored the importance of quality control in estimating UHI effects, as continuous monitoring of land surface temperatures is a relatively new luxury when attempting to use historic data in analyzing changes and trends in UHI. The authors concluded that satellite-based land surface temperature measurements should be used with caution, as they are prone to quality control issues, although there does not appear to be any specific correlation between increased QC concerns and changes in the time of day/year/weather conditions. Around the same time, Li et al. (2018) developed a 1 KM resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States, which used among other things satellite based LST measurements in its analysis. Based on other research on microclimates and the significant variability found within buildings, parking lots, etc. on UHI, this dataset is probably best used for high level analysis, and has little use with regard to building practices and UHI mitigation strategies, but may be useful for regional spatial analysis.

Mackey et al. (2012) focused on the cooling effects of city-scale efforts to reduce the urban heat island, using remote sensing techniques to attempt to analyze what cooling strategies helped with UHI mitigation the best. Using Landsat data, their data showed that the albedo effects of rooftops were more ideal than an increase in vegetation. This may seem like a counter to other research, but in the context of using remote sensing, it makes sense, as Sharma (2016) found that green and cool roofs reduced vertical wind speeds and vertical mixing during the daytime led to stagnation of air near the surface, potentially causing air quality issues. This represents a great research opportunity for one to try to find out how to mitigate this negative externality of UHI mitigation, if possible, without disrupting the UHI benefits. However, green roofs still produced a smaller but noticeable impact on the Urban Heat Island, so it remains a viable strategy for mitigation.

Gao et al. (2015) used a remote sensing approach as well in attempting to measure the urban heat island, and compared two approaches, using a density slicing approach which measured pixel by pixel of a satellite image with object-oriented segmentation, which instead attempts to characterize objects in the frame.

This approach yields a more homogeneous raster dataset which can be easier to comprehend for UHI mitigation strategies. This strategy is designed for a high-level view of UHI impacts, however, considering the research of Alessandro et al. (2022), where internet-of-things devices were used to measure hyperlocal UHI impacts in city structures, such an approach would not be suitable to analyze the UHI at that level.

Study Area: Chicagoland Region

The study area is the Chicago metropolitan area, as shown in Figure 1. The study encompassed the city of Chicago and its surrounding suburbs within the six-county region known as “Chicagoland”. Latitude and longitude coordinates, and all maps presented, are based on the NAD 1983 geographic coordinate system. The project data, including county boundaries, land use inventory, weather daily summaries, station locations, and locales are shown in Table 1.

Data Sources

Meteorological Data

National Weather Service (NWS) Daily Summary Data (Data ID 3 in Table 1) for minimum temperatures were obtained for stations across the region. The station names and coordinates in this study are shown in Table 2. Minimum temperature reading dates were selected based on many of the warmest temperatures observed at O’Hare International Airport (ORD) between 2019-2022. 2023 data were available; however, as of the conclusion of this project, no nights of significant warmth have occurred at O’Hare airport to warrant inclusion in this list. The dates included within Data ID: 3 in Table 1 match the minimum temperature dates of the study. These coordinates are mapped within the study area below in Figure 2.

Figure 1 – Map of the study area: Cook, DuPage, Kane, Lake, McHenry and Will Counties, referred to as “Chicagoland.”

Land use and land cover data for the Chicago metropolitan area were acquired from the Chicago Metropolitan Agency for Planning (CMAP) as of 2018 (Data ID: 2). This data provides detailed information on the different land cover types and their spatial distribution within the study area. The map of land use and land cover after simplification is shown in Figure 3.

Table 1 – Sources of data used in this project.

Data ID:DataSource:Date of Data:Link to Source:
1Illinois State and County Boundaries (clipped)Illinois State Geological Survey2003
22018 Land Use Inventory (clipped)Chicago Metropolitan Agency for Planning (CMAP)06/30/2023

3Weather Daily SummariesNational Center for Environmental Information (NCEI)Many
4Weather Data Location SummariesNational Weather Service07/15/2023
5Locale Boundaries (clipped)National Center for Educational Statistics2021

Table 2 – Data Stations and L
atitude/Longitude Locations

BARRINGTON 3 SW42.1153-88.1639
MIDWAY AIRPORT41.78412-87.7551
NORTHERLY ISLAND41.8558-87.6094
PALWAUKEE AIRPORT42.12076-87.9048
CRYSTAL LAKE 4 NW42.2611-88.3952
MUNDELEIN 4 WSW42.25515-88.0776
PARK FOREST41.4947-87.6802

Figure 2 – Map of the weather stations in this study.

Locale Data

The study used locale data from the National Center for Educational Statistics (Data ID: 5) as of 2021. This data provides information on the classification of the municipality based on population density: rural, town, suburban, and city. This map is shown in Figure 4.

This project will select minimum temperature data from the National Weather Service at O’Hare airport, Chicago’s official station, and compare it with other area weather stations.

Figure 3 – Map of the study area including land use and land cover.

National Weather Service (NWS) Daily Summary Data (Data ID 3): Daily minimum temperature data was obtained for stations across the region, and joined to the station location data (Data ID: 4 in Table 1) via the common field “STATION_ID”. The station location data has elevation of the station included in meters. This was changed to feet to use entirely Imperial units in the study.

O’Hare Airport was chosen as the baseline for this study as it is the official station for temperature and other meteorological data for the City of Chicago. However, having the official observation site for the city of Chicago has the potential mask urban heat island effects in the central part of the city. O’Hare airport is located well inland, away from both Lake Michigan and potential sources of urban heat island effects, and as a result, the official temperature reading at O’Hare Airport often does not, nor does it intend to, showcase the entire distribution of temperatures across the region, including the urban heat island effect within the city of Chicago.

Figure 4 – Map of the study area municipality classification.

Data Processing and Analysis

Meteorological Data

Minimum temperature data for the nights shown in Data ID: 3 were collected via the National Center for Environmental Information (NCEI) mapping tool, and joined to their respective station location data for Data ID: 4 in Table 1. Once the data was joined, the file was converted into a shapefile and plotted to the region. The data were formatted to round to the nearest whole number and are presented in Imperial units, including feet for elevation and Fahrenheit for temperature.

Land Use and Land Cover Data

Land use data from the Chicago Metropolitan Agency for Planning (CMAP) were obtained (Data ID: 2 in Table 1). The land use data provides information on the spatial distribution and characteristics of different land cover types within the study area. The data gathered were simplified into the following types of land use: residential, commercial, industrial, agricultural, institutions, recreational, vacant/other/unknown, transportation and infrastructure, and water. This simplification was done by using unique values symbology and grouping relevant feature classes together. A 1000 ft buffer of the area around each weather station point was created. A second spatial join was created between the weather station buffer polygons and the land use using largest overlap classification for the land use. By comparing the land use and land cover data with the distribution of nighttime temperatures across the study area, areas with potential mitigation strategies for the urban heat island effect can be identified.

Locale Data

The study used locale data from the National Center for Educational Statistics (Data ID: 5 in Table 1) to classify each locale as rural, town, suburban, and city, and each type is divided into three subtypes based on population size or proximity to populated areas. These data were clipped into the Chicagoland region and are used to classify each weather station based on the subtype. This classification is done via a spatial join.

Statistical Analysis

Once the minimum temperature data for each study date, prevailing land use, and municipal classification data were collected, a statistical analysis of the minimum readings was performed, using the mean, median, mode, minimum, maximum, range and standard deviations of each day during the study period.

Results of Low Temperatures Across the Region

The results of this study are first presented in Table 3 showing the minimum temperatures of each weather station on the dates collected for this study. The official O’Hare Airport reading is boldened; and these readings were used to compare the differences in results of low temperatures across the region. Of the dates studied, Midway Airport had the lowest range in minimum temperature values, from 75 to 83 degrees, respectively, while Northerly Island had the greatest range, from 58 to 82 degrees. A detailed statistical analysis of this data is presented in Table 4.

Area Analysis

Figure 6 displays the lowest minimum temperatures observed during the study, and which day they occurred on. The area wide minimum temperatures were mapped in Figure 7. Within the study of minimum temperatures during days of the warmest minimum temperatures at O’Hare Airport, Crystal Lake, located in the far northwestern suburbs of Chicago and far away from the most prevalent Urban Heat Island effects, had the lowest average minimum temperatures in the study period, at 67.7 degrees compared to O’Hare’s 75.2-degree average. However, the lowest recorded temperatures were located in close proximity to Lake Michigan on May 11, 2022 at Waukegan (57 degrees) and Northerly Island (58 degrees).

Table 3 – Data Stations, Elevation, and minimum temperature readings for the selected study dates.

StationElevationTemp 7/9/19Temp 7/6/20Temp 8/25/20Temp 7/6/21Temp 5/11/22Temp 6/15/22Temp 8/3/22Temp 8/4/22Temp 8/7/22Temp 8/27/22
PALWAUKEE AIRPORT62982737178707676687478
MIDWAY AIRPORT61081767777778376757876
OHARE INT’L AIRPORT67281757277747675697578
WEST CHICAGO DUPAGE AIRPORT74981686774717673667374
NORTHERLY ISLAND58380757377588274697177
WAUKEGAN REGIONAL AIRPORT71079686766577176647376
AURORA MUNICIPAL AIRPORT70178666770727570677373
JOLIET BRANDON ROAD LOCK DAM54372706970677665717171
LISLE MORTON ARBORETUM67771666272697866727274
PARK FOREST71071686870657665707171
BARRINGTON 3 SW87570696572707467717173
MC HENRY WG STRATTON LOCK AND DAM73670656473737568717073
MUNDELEIN 4 WSW84070666574687563717173
CRYSTAL LAKE 4 NW94069656669707063737062

The May 11, 2022 date had the largest range of area temperatures, from the aforementioned 57 degrees at Waukegan to 77 degrees at Midway Airport. The Northerly Island low reading reflects the nature of its proximity to Lake Michigan, and the fact that the reading occurred very early in Chicago’s warm season, where the Lake was still quite cool in comparison to the surrounding air. This resulted in a 19-degree temperature differential between Northerly Island and Midway, despite being less than ten miles apart. The same forces acted upon Waukegan, however, Waukegan was consistently much cooler than O’Hare Airport during the days studied, while Northerly Island usually had temperatures much closer to O’Hare’s official reading. It is noteworthy that despite being the earliest date observed, the only sites where the lowest temperature occurred were in relative proximity to Lake Michigan, and removed from other heat island forcings (Northerly Island, Waukegan and Park Forest).

Table 4 – Mean, Median, Mode, Minimum, Maximum, Range and Standard Deviations of Temperature Readings from weather stations in this study:

StationMeanMedianModeMinMaxRangeStandard Deviation
CHICAGO PALWAUKEE AIRPORT74.675786882144.029888
CHICAGO MIDWAY AIRPORT77.67776758382.374868
CHICAGO NORTHERLY ISLAND73.674.5775882246.390618
JOLIET BRANDON ROAD LOCK DAM70.270.5716576112.785678
LISLE MORTON ARBORETUM70.271.5726278164.354308
PARK FOREST69.570716576113.074085
BARRINGTON 3 SW70.270.570657492.56125
MC HENRY WG STRATTON LOCK AND DAM70.270.5736475113.429286
MUNDELEIN 4 WSW69.670.5716375123.8
CRYSTAL LAKE 4 NW67.769706273113.348134

Most sites in this study had temperatures below the official reading at O’Hare International Airport; but the warmest readings were consistently located at Midway Airport, which is located in a more central area within the urban fabric of Chicago. On 9 out of 10 days studied, Midway’s minimum reading was higher than O’Hare’s, and in several cases, was higher by a difference in over 5 degrees Fahrenheit, the most significant of which occurred on June 15, 2022, where O’Hare recorded a 76-degree overnight reading, however Midway’s only dropped to 83 degrees in comparison. Northerly Island was also very warm that night, dropping to only 82 degrees, and thus receiving almost no cooling effects from its proximity to Lake Michigan, despite the much cooler reading on May 11th.   

Figure 6 – Map of lowest minimum temperatures observed during the study, and which day they occurred on.

Figure 7 – The mean minimum (low) temperature readings during the dates of the study.

Elevation Analysis

The differences in elevation within each site in the study area were less than 400 feet total, with the lowest elevation at Joliet Brandon Road Dam (543 feet) and Crystal Lake (940 feet) showing no striking variations in temperature, implying that the relative lack of elevation change within the Chicagoland area in comparison to other regions is not a significant factor in the urban heat island effect. That said, Alessandro et al. (2022) did conclude that elevation relative to hyperlocal surroundings did impact the urban heat island effect, but without sensors in close proximity to one another, it is impossible to analyze the effect height and elevation played. Figure 8 shows the trend of mean low temperatures compared to elevation. Despite the relative lack of elevation change within the study area, there is a trend between the lowest and highest weather station points in the region, although this is likely due to the fact that the highest points within the region are located north and west of Chicago, where the urban heat island effect is not as prevalent.

Figure 8 – The mean minimum (low) temperature readings compared to elevation.

Land Use and Land Cover Analysis

Each of the largest land uses for the weather station within a 1000’ radius is shown in Table 5 based on the CMAP Data (Data ID: 2 in Table 1). The airport land use type for the weather stations occurred most often in this study, as the National Weather Service typically locates these stations near airports to provide aviation audiences with appropriate weather conditions. The open space an airport provides also can reduce the urban heat island effect by not having air trapped in vertical forcings upon buildings, one of the key drivers of UHI affect according to Coseo and Larsen (2014). However, this also can trap heat that would otherwise be absorbed in more natural environments, such as forests or agricultural areas.
Table 5 – Prevailing Land Use Types Within a 1000’ Radius of Each Weather Station

StationLand Use Type
PARK FORESTResidential
BARRINGTON 3 SWRecreational (Forest)
MUNDELEIN 4 WSWAgricultural
CRYSTAL LAKE 4 NWAgricultural

Agricultural land at Mundelein and Crystal Lake were some of the lower average readings in comparison to the minimum nightly readings at O’Hare, however, this is also explainable as each station is far north and west of the city of Chicago.  Of the forested areas, Lisle Morton Arboretum is closest in proximity to O’Hare, and close enough to the urbanized area of Chicago to be most influenced by the UHI. The station, as well as Barrington and McHenry, all displayed mean values five degrees below that of the average minimum temperature at O’Hare airport during the study, with Lisle having the warmest reading of the three forested areas with a 78-degree minimum on June 15, 2022.

Difference in temperatures from O’Hare Airport

Table 6 and Figure 9 presented below illustrate the temperature variations recorded at each of the local area weather stations in comparison to the official readings reported at O’Hare International Airport. The data collected represents a comprehensive analysis of temperature deviations over each data of the study. Table 7 represents a statistical analysis of the differences in temperature in comparison to the readings at O’Hare airport throughout the study dates.

Figure 9 – The differences in temperature readings at the NWS locations compared to the official reading at O’Hare International Airport, the official Chicago station.

Owing to the influence of Lake Michigan, especially in the case of the Northerly Island weather station, the range in temperatures when compared to the minimum temperature at O’Hare varies greatly throughout the region. However, generally speaking, the range in temperatures increases as one gets farther away from O’Hare. The closest station to O’Hare, Palwaukee, only had a maximum range of 5 degrees Fahrenheit difference compared to the observed minimum temperatures at O’Hare. Nonetheless, Airport land uses had the smallest range in comparison to O’Hare temperatures, and this pattern is observed even in comparison to proximity, as one of the farthest stations from O’Hare, Aurora Municipal Airport, had only an 8-degree maximum temperature variation compared to O’Hare’s minimum temperatures.

Of the 16 locations studied, only Midway Airport had a higher mean minimum temperature compared to O’Hare (+2.4 degrees), and along with Northerly Island, was only one of two locations within the City of Chicago to be measured. Had the May 11, 2022 data for Northerly Island not been counted in the study, however, Northerly Island would have also had a higher average mean temperature than O’Hare, as the temperatures at O’Hare and Northerly Island were well correlated with the exception of that night due to the lake forcings. Within the study, the most closely correlated stations to O’Hare were Palwaukee (-0.6 degrees), Northerly Island (-1.6 degrees), and Romeoville (-1.9 degrees).

The range of temperatures across the 16 studied locations exhibits significant variability, but the temperatures with the lowest variability are located away from the Lake but still within Chicago’s urban heat island forcing. Including the May 11, 2022 reading in the results significantly increased the standard deviation that was observed at Northerly Island and Waukegan, which were significantly influenced by their proximity to Lake Michigan.

Area Pattern

As shown in Figure 8, there exists a negative correlation between elevation and mean low temperatures, however, the small difference between the lowest and highest points in the region means that this correlation is more coincidental and a product of the elevation geography, where the highest points in the region are located away from the greatest urban heat island factors. Figure 10 compares mean minimum temperatures across the region with the municipality classification. Although the number of observed temperature sites in the Chicagoland region is relatively small, a clear link can be established between the major city (Chicago) and the rest of the region, mostly composed of Large Suburbs interspersed with Small/Midsized Cities. The two warmest sites in the study are O’Hare Airport and Midway Airport, both located within the City of Chicago, while the rest of the region’s minimum temperatures are lower by comparison.

Figure 11 shows the land use of each weather station as well as the mean low temperature. The locations closest to Lake Michigan, Northerly Island and Waukegan, also show the largest range in their data, which confirms water as a land cover is a significant variable in urban heat island impact. However, without an appropriate breeze, this can provide little in mitigation of urban heat island, as Northerly Island registered the second-warmest 82-degree minimum reading (tied with Palwaukee) in this study. The airports of Palwaukee, Midway, DuPage, Aurora Municipal as well as Romeoville (University but adjacent to Lewis University airport) experienced the least range compared to O’Hare readings.

Table 6 – Differences in Temperature in comparison to the official reading at O’Hare International Airport.

Station 7/9/19 7/6/20 8/25/20 7/6/21 5/11/22 6/15/22 8/3/22 8/4/22 8/7/22 8/27/22
PALWAUKEE AIRPORT1-2-11-401-1-10
NORTHERLY ISLAND-1010-166-10-4-1
WAUKEGAN REGIONAL AIRPORT-2-7-5-11-17-51-5-2-2
AURORA MUNICIPAL AIRPORT-3-9-5-7-2-1-5-2-2-5
LISLE MORTON ARBORETUM-10-9-10-5-52-93-3-4
PARK FOREST-10-7-4-7-90-101-4-7
BARRINGTON 3 SW-11-6-7-5-4-2-82-4-5
MC HENRY WG STRATTON LOCK AND DAM-11-10-8-4-1-1-72-5-5
MUNDELEIN 4 WSW-11-9-7-3-6-1-122-4-5
CRYSTAL LAKE 4 NW-12-10-6-8-4-6-124-5-16

The research encountered some limitations, such as the lack of city-level temperature stations, which made it challenging to assess temperature variations within the urban core. Thus, this analysis did not consider the influence of microclimates which would certainly impact the urban heat island at a smaller scale than the study. Using population or land use density data as opposed to just the simple land use, may have yielded a more significant link between urban density and land use than what was observed in the study.

Additionally, as the study focused on ten warm nights where the minimum reading at O’Hare Airport was exceptionally warm, a more robust regional temperature analysis could provide further context as to the variability within Chicago’s regional urban heat island effect, such as when significant temperature deviations from the O’Hare Airport reading are most likely to occur. A further limitation in the study was the lack of city-level temperature stations, with only the official O’Hare location, Midway and Northerly Island are located within the City of Chicago. This limitation is somewhat offset in that many of the stations are located in large suburban areas of the region that is largely residential in nature, however, the area-wide analysis still leaves gaps in neighborhood observations that may be more beneficial in finding the most extreme urban heat island impacts within the city.

Figure 10 – The mean minimum (low) temperatures observed at each weather station with municipality classification (locale).

Finally, historical analysis of warm nighttime temperatures from decades prior to 2018, and how those fluctuations compare to the study may also prove valuable in determining how Chicago’s urban heat island influence is changing over time. It is likely that as development has spread outward throughout the region from Chicago’s urban core over time, the impact of the urban heat island is likely also increasing in newly developed parts of the area.

Table 7 – Mean, Median, Mode, Minimum, Maximum, Range and Standard Deviations of Temperature Differences from O’Hare Airport at weather stations in this study:

StationMeanMedianModeMinMaxRangeStandard Deviation
CHICAGO PALWAUKEE AIRPORT-0.6-0.51-4151.496663
CHICAGO NORTHERLY ISLAND-1.6-0.5-1-166225.351635
LISLE MORTON ARBORETUM-5-5-10-103134.472136
PARK FOREST-5.7-7-7-101113.689173
BARRINGTON 3 SW-5-5-5-112133.316625
MUNDELEIN 4 WSW-5.6-5.5-122144.152108
CRYSTAL LAKE 4 NW-7.5-7-12-164205.239275

This study investigated the variations in minimum nighttime temperatures across the Chicagoland region. The study analyzed daily minimum temperature data collected from multiple weather stations across the area, with O’Hare International Airport serving as the reference point due to its status as the official temperature station for the city of Chicago. The research also incorporated land use and land cover data as well as locale data to examine how different factors might influence temperature patterns in the study area. The study demonstrated significant variations in minimum nighttime temperatures across the Chicagoland region. Midway Airport consistently recorded higher temperatures compared to O’Hare Airport, indicating the influence of urban heat island effects within the city of Chicago.

Figure 11 – The mean minimum (low) temperatures observed at each weather station with land use classification.

Stations located close to Lake Michigan, such as Northerly Island and Waukegan, showed larger temperature ranges, demonstrating the moderating effect of the lake on temperatures during different periods. However, this effect was not consistently observed, as some nights showed higher temperatures at these lakefront stations due to local conditions. A potential future study would be to examine if this moderating effect is changing with regard to historical temperature readings, and to analyze why such an effect is changing significantly or not.

The research highlighted the influence of land use and land cover on minimum nighttime temperatures. Stations near airports and open spaces generally had lower temperature ranges, likely due to reduced vertical forcings on buildings and the absence of immediate urban heat island effects. Forested areas showed slightly cooler temperatures compared to other land use types. Urban Heat Island Mitigation: The study suggested that certain land use types, such as agricultural and forested areas, could potentially contribute to mitigating the urban heat island effect in their respective regions. In future studies, a more granular approach may be more useful to determine the urban heat island impact across the neighborhoods within the city of Chicago, as, for example, the temperature variations between Aurora and Aurora Municipal Airport, demonstrated.

NOTE: A methodology flowchart, figure 5, has been removed from this research for clarity.


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