2.1. Health Consequences of Particulate Matter (PM2.5)
A number of epidemiological studies have been carried out to date demonstrating the impacts of deteriorated ambient air quality on human health, although the health effects of fine particulate matter depend on four factors: the source and composition of particulate matter, the time of exposure, its depth of travel inside the human body, and the age of the affected person. Persistent exposure to human-induced PM
2.5 is positively associated with a series of pulmonary and cardiovascular diseases. For instance, in children, chronic exposure to PM
2.5 poses risks for acute lung respiratory infections (ALRI), while in adults, it can generate chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), cardiopulmonary disorders, lung cancer, and stroke [
17,
18,
19]. Pope et al. [
20] highlight that there is a 4–6% increased risk of cardiovascular and lung cancer mortality with each 10 mg/m
3 increase in fine particulate matter in the air.
Ambient air pollutants might not only harm physical health, but can also cause damage to people’s nervous systems and cognitive abilities, leading to neurological disorders of different magnitudes, ranging from headaches and migraines, to strokes and various types of dementia [
21]. In addition, a few studies have stated that the PM
2.5 in the air contains certain neurotoxicants that can either produce or accelerate neurodegenerative diseases related to cognitive decline, schizophrenia, and brain damage. These studies strongly suggest that persistent exposure to the fine particulate matters present in ambient air might harm the central nervous system [
22,
23,
24,
25,
26]. A recent study by Ranzani et al. [
27] reported that adult human lungs have the capacity to purify 10,000 liters of air daily; however, increasing levels of pollutants could reduce immunity, resulting in increased inflammation and poor bone health.
2.2. Campus Response to Challenges of Poor Ambient Air Quality
Educational administrators have launched several initiatives to tackle toxic particulate matter and the other pollutants present in ambient air. The low-carbon campus project at the Massachusetts Institute of Technology [
28] is an endeavor of the MIT Office of Sustainability (MITOS) to establish a healthy and low-carbon campus by exploiting the competence and experience of prestigious MIT alumni to create the testbed for expandable solutions. Four core components were explored to accomplish the goal of carbon neutrality at the MIT campus, including mobility, building, climate, and energy. Under the mobility theme, the students, faculty, and staff members of MIT are encouraged to select flexible, affordable, and low-impact modes of transportation, including walking and bicycle riding, with only a small number of members commuting by car.
Similarly, the University of Leeds [
29] launched a living lab for their air quality project in 2017 that uses the inhouse air quality and pollution to enhance the environment and health of the community’s members. The living lab regularly helps to diminish emissions from vehicles and curb exposure to poor air quality. In a recent related project, the Institute for Transport Studies has been investigating pollution exposure to university staff, faculty, and students via commuter routes. Volunteers carry air quality monitoring devices while walking, using public transport, or driving; the results are used to make comparisons on the levels of pollution exposure between different transport modes and routes.
It is also noteworthy that smoking emits 10 times more air pollutants into the ambient air than a car. The smoking of one cigarette daily produces an equivalent PM
2.5 level of 22 μg/m
3 [
30,
31]. Considering this, universities, including KAU [
32], have formulated strategies to create smoking-free campuses. In compliance with the Smoke Free Environments Act, 1990, of New Zeeland, which prohibits smoking in workplaces, Lincoln University [
33] accepted the “Clean Air Policy”, aimed at providing a healthy and safe smoke-free working and learning environment on their campus.
Moreover, universities in developing economies are the worst affected by PM
2.5 exposure and are struggling to transform their campuses towards carbon neutrality. According to Express Web Desk [
34], the University of Hyderabad in India has recently introduced e-rickshaw services on weekdays from 8:00 a.m. to 6:00 p.m. This affordable (USD 0.14 per trip) and zero-emission commuting service might improve ambient air quality inside the university campus. Some other universities are trying to increase awareness about air pollution by disseminating and displaying information on air quality. The Central University of Columbia at Bogota has established an Air Quality Monitoring Network (Red de monitoreo de calidad del aire) that is equipped with low-cost sensors and Internet of Things technology [
35]. The Times News Network [
36] reported on Punjab University at Chandigarh (India) as an excellent example of this, as they have set up a Continuous Ambient Air Quality Monitoring Station (CAAQMS) that offers hourly data in real time. Air quality information is thus available to everyone through large electronic display panels. Xi’an Jiao Tong University in China is transforming its campus into a green energy-fueled smart campus, aiming to enhance ambient air quality for the academic community [
37].
Furthermore, Monash University [
38] has suggested that a future without change will be dismal; on the dangers of air pollution, they state, “We don’t believe in a future where people can’t go outside”.
If the current trends of air pollution continue, then breathing in ambient oxygen might significantly risk the health of people. The Monash Climate and Air Quality Research Group (CARE) maintain that the fine-particulate matter in ambient air can significantly accelerate the risk of miscarriage among pregnant women, and the group also found a robust association between air pollution and autism. In their plan to combat air pollution, Monash set the goal of attaining net zero emissions on Australian campuses by 2030. In Canada, the University of Victoria [
39] has developed a Campus Cycling Plan to make their campus bicycle-friendly, with the goal of maximizing cycling, walking, public transit use, and carpooling up to 70% by expanding facilities for cycling by 10%. To promote bicycle riding in and around the campus, the university plans to improve the cycling network, the safety of bike-users in shared spaces, bicycle parking, bicycle sharing, and end-of-trip amenities for users of all age groups.
The Surgeon General [
40] of the USA has urged American universities to construct walkable campuses, suggesting that walking is a win–win strategy for community health, as increased physical activity offers significant health benefits. Bopp, Kaczynski, and Wittman [
41] suggest that colleges and universities should become the ultimate locations for walking. Policies related to walkable campuses may not only inspire students, faculty members, and personnel to embrace active living, but may also encourage students to consider future roles as public health professionals, urban planners, urban designers, transport planners, and architects. Stevens [
42] reported that the University of Kentucky has installed a large amount of signage on their campus, with QR codes in collaboration with the WALK [Your City] app, which helps university students approximate the time required for traveling by foot as an alternative to driving. This also helps university researchers in their investigations into how university attendees use information technology to plan their day. Scott et al. [
43] state that Canadian universities are working to make their campuses car-free. The University of British Columbia [
39] has been successfully implementing its Transport Strategic Plan (TSP) since 1999 (reviewed in 2005). UBC has a large cycling and pedestrian network and is aspiring towards sustainable campus transit by 2040.
2.3. PM2.5 Modeling and Geographically Weighted Regression
Several studies before now have investigated the spatial heterogeneity and spatial dependence of PM
2.5 on the associated socioeconomic and environmental factors, using geographically weighted regression (GWR). GWR permits the exploration of spatially varying relationships [
44]. Nearly all the studies have validated that GWR addresses the implicit spatial attributes of PM
2.5 data, and improves upon the outcomes offered by traditional OLS regression, which is nonspatial in nature [
45].
Lin et al. [
46] emphasize the urban green belt area, population density, and economic growth as the key factors affecting the concentration of PM
2.5 in Chinese cities. Guan et al. [
47] stressed that China’s foreign trade is responsible for most of the PM
2.5 pollution. According to Hao and Liu [
48], motor vehicles and industrial activities are the factors of PM
2.5 exposure. Zhang et al. [
49] deployed the enhanced vegetation index (EVI) with GWR, and concluded that meteorological parameters, together with fused aerosol optical depth (AOD) products, explain nearly 87% of the spatial variance in PM
2.5 concentrations. Similarly, Pateraki et al. [
50] concluded that humidity and temperature fluctuations were strongly correlated with PM
2.5 concentration, while Onat and Stakeeva [
51] affirmed that accelerated wind speed (>2m/s) might significantly lower the intensity of PM
2.5.
In recent years, researchers have frequently used GWR models to understand PM
2.5 exposure in various cities and regions. Through a generalized additive model (GAM), He and Lin [
52] confirmed that the PM
2.5 concentration change in Nanjing was strongly correlated with air pressure, water vapor pressure, and temperature. The seasonal and daily variability in PM
2.5 levels was modeled by several spatial scientists in the Yangtze River delta region via GWR, while the spatiotemporal mapping of fine particle concentrations in mainland China was carried out by combining Bayesian maximum entropy (BME) with GWR [
53,
54].
Many types of GWR models have been effectively employed to quantify the spatiotemporal heterogeneity of PM
2.5 pollution in Chinese cities. Zhai et al. [
55] developed an enhanced-subset regression model, which combines Principal Component Analysis (PCA) and GWR to predict the independent variables responsible for spatial variations in the levels of PM
2.5. Hajiloo, Hamzeh, and Gheysari [
56] developed models to understand the impacts of metrological and environmental parameters on the intensity of PM
2.5 using satellite data and GWR analysis. Other GWR-based studies by Cheng et al. [
57], Dong et al. [
58], and Lou et al. [
59] demonstrated the various determinant factors responsible for the geographical heterogeneity of PM
2.5.
A recent study by Gu et al. [
60] suggested that PM
2.5 increases in Chinese cities are positively associated with people’s income; growths in income in certain geographical areas have aggravated PM
2.5 emissions. Wang and Wang [
61] observed that the density of the population, the proportions of industrial land uses, car ridership, and the amount of foreign direct investment (FDI) all contribute significantly to the level of PM
2.5, and show qualities of spatial heterogeneity. There were also significant variations in the levels of influence of these factors between different time periods and locations.