The term "microclimate" refers to the climate conditions in a specific, localized area, and changes in these conditions can impact various aspects of urban life. Microclimate refers to localized climate conditions near the Earth's surface, including temperature, light, wind speed, and moisture. A microclimate is a set of climatic parameters that fluctuate within a short region, including temperature, humidity, wind speed, and precipitation. Ecosystems, human health, and socioeconomic activity can all be significantly impacted by these localized differences. These environmental variables have been crucial for human history, influencing habitat selection and activities. Shirley's studies emphasize microclimate's influence on ecological patterns, plant and animal growth, soil respiration, and nutrient cycling(Chen et al., 1999). For instance, rising temperatures may contribute to the urban heat island effect, increasing residents' heat stress. Changes in air quality can have health implications, and disruptions in other microclimate factors may affect the overall environmental balance (Kousis et al., 2021). Studying the dynamics of microclimates is crucial as they shape regional weather patterns and climate resilience. The characteristics like geography, land use, and urbanization, microclimates can diverge significantly from the surrounding regional climate. A comprehensive understanding of microclimate dynamics is essential for many fields, such as agriculture, public health, urban planning, and environmental science.
Climate change is correlated with heightened occurrences and intensity of microclimate alterations globally. The surge in global temperatures and shifting weather patterns contribute to a more frequent and severe urban heat island effect (Kyselý, 2007). Microclimate factors influence decisions related to building layout, green infrastructure, and heat-reduction techniques in urban planning. Which also have an impact on air quality, heat-related illnesses, and the transmission of disease in public health. Adapting to and lessening the impacts of climate change is imperative to mitigate the risks associated with urban microclimate changes. Strategic planning and effective responses to climate change become pivotal in minimizing the threat of heat-related fatalities in urban areas (Ahmadalipour and Moradkhani, 2018).
Microclimate research focuses on understanding localized climate conditions and their impact on ecological, cultural, and human systems. Statistical approaches are crucial in analyzing spatio-temporal dynamics of microclimates. Innovative models and techniques have been introduced to analyze dependencies and spatio-temporal dynamics in environmental data. Understanding microclimate variation is essential for terrestrial organisms' survival and function. Statistical methods have been developed to compare different microclimates, providing insights into their potential impact on ecological patterns and cultural heritage. These studies highlight the importance of statistical approaches in advancing microclimate research. Exploring the statistical tools of Copulas and Extreme Value Theory (EVT) in the context of urban microclimate analysis in Python unveils sophisticated methodologies for capturing complex dependencies and extreme events. The Generalized Autoregressive Conditional Heteroskedasticity-Extreme Value Theory-Clayton model (Kamal and Paul, 2024) illustrates how copulas enable the estimation of liquidity-adjusted value-at-risk for energy stocks. Meanwhile, the utilization of EVT in predicting worst-case convergence times of machine learning (Tizpaz-Niari and Sankaranarayanan, 2024). This integration of Copulas and EVT in urban microclimate analysis not only enriches the modeling of intricate interrelationships but also enhances the prediction and mitigation of extreme climatic events, emphasizing their significance in advancing data-driven insights and decision-making processes. Incorporating insights from studies on the impact of greenery on urban health (Sędzicki et al., 2023) and the construction of Urban Health (UH) indices (Mahakalkar et al., 2023) a multidimensional approach can be formulated. By leveraging the principles of Conceive, Design, Implement, and Operate (CDIO) framework alongside advanced methods like Software in the Loop (SiL) and Hardware in the Loop (HiL), researchers can develop automated greenery design methodologies tailored for dense urban spaces.
Dionisi-Vici et al. (2011) used statistical methods to compare microclimates, analyzing wooden objects sensitivity to environmental fluctuations, and providing insights into ecological patterns and cultural heritage. Some studies utilized a self-assembled system to monitor temperature, humidity, and luminance in a complex construction, revealing varying responses in indoor and outdoor environments, emphasizing the importance of sampling design and instrument validation(Visco et al., 2012). Calculli et al. (2015) introduced a multivariate generalization of the hidden dynamic geostatistical model, enabling analysis of dependencies and spatio-temporal dynamics in environmental data, including air quality. Tonini et al. (2016) analyzed spatio-temporal techniques for reconstructing near-surface air temperature in northern California, highlighting the effectiveness of empirical orthogonal functions and providing practical guidelines for data gaps. The Internet of Things (IoT) is crucial for real-time environment monitoring, especially in urban areas. An integrated geovisualization framework combines computational intelligence and visual methods to analyze urban microclimate patterns(Rathore et al., 2018). Indoor microclimate diagnosis helps understand indoor-outdoor building interactions and hygrothermal variability, evaluating long-term variability and identifying sources of infiltrative water (Litti and Audenaert, 2018).
A web-based microclimate estimation procedure has been developed for the r programming environment, integrating existing r packages and two microclimate modeling packages (NicheMapR and microclima). This allows for realistic estimates of microclimate at fine spatial and temporal scales(Kearney et al., 2020). Duffy et al. (2021) highlighted the significance of understanding microclimate variation for terrestrial organisms' survival and function, suggesting drone data and established techniques for comprehensive understanding.
The present study focuses on developing prediction scenarios (5-year return value) for evaluating the effects of microclimate changes on ecosystems and human activity, and comprehending how different causes influence microclimate variability. The study aims to enhance comprehension of microclimate dynamics and offer recommendations for decision-making that will enhance sustainability and climate resilience in urban and rural regions. Predictive modeling, impact evaluation, hotspot identification, temporal trend analysis, and spatial analysis are all combined to achieve this.
Previous studies on urban microclimate dynamics, while insightful, often faced constraints such as limited spatial coverage, inadequate data integration, and insufficient statistical analysis. These limitations underscore the necessity of our present study, which takes an integrated approach to address these shortcomings. Prior research has used fewer monitoring stations or focused on narrower geographic regions, resulting in a limited understanding of microclimate variations in metropolitan areas. In contrast, our study leverages data from 40 CPCB monitoring stations in Delhi, offering a more representative examination of microclimate dynamics and broader geographical coverage. Additionally, previous studies may have overlooked the importance of evaluating the statistical consistency and stationarity of key variables, essential for understanding long-term trends. Our study remedies this by employing statistical techniques like the KS test and the ADF test, enhancing the validity of our conclusions. Moreover, while past research examined temporal trends in atmospheric parameters, it may have neglected the impact of changes in Land Use and Land Cover (LULC) on microclimate dynamics. By integrating an analysis of LULC changes with annual trend analyses like the SQMK test, our study provides insights into the intricate relationships between urbanization, land cover changes, and climatic fluctuations.
The application of copula analysis in urban microclimate studies offers a powerful tool for understanding the complex relationships between various meteorological parameters in urban environments. By using copulas, researchers can model the joint distribution of multiple variables such as temperature, humidity, wind speed, and solar radiation with greater accuracy compared to traditional methods. This allows for a more comprehensive investigation into the interactions and dependencies between these parameters, leading to improved predictive models and better-informed urban planning decisions.