Bingqing Liang

Elevated urban temperatures are a significant concern across the globe due to their negative health effects and increased energy use. Understanding the spatial variation in urban air temperatures can lead to informed mitigation and planning efforts. Air temperatures for multiple urban areas in the state of Iowa, USA, at three times of the day, were collected using customized sensors mounted on vehicles driven through a variety of landscapes in each urban area. Geographic information systems technology was used to process high-resolution landscape datasets and derive variables that summarize the urban landscape surrounding each temperature measurement point. Five different statistical models: standard regression, trend surface, geostatistical, time series, and random forest, were fitted to nighttime data in the Waterloo–Cedar Falls urban area. We demonstrate that the best method for predicting Waterloo–Cedar Falls nighttime data is to use Waterloo–Cedar Falls data collected at a different time of day. However, when data are not available in the same city for which predicted air temperatures are needed, we explore which substitute city’s data best forecast the target city’s air temperature, via four cross-validation strategies. We find that, when predicting evening and nighttime air temperatures for the Iowa urban areas, choosing the closest-in-population-size substitute city provides the best predicted air temperatures.

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m2 aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R2 coefficients ranging from 0.879 to 0.997 with the majority exceeding an R2 of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.

With cities experiencing faster warming rates than their surroundings and two-thirds of the global population projected to be living in urban areas by 2050, studies that help to describe in detail the spatial temperature patterns in the urban environment have the potential to address concerns regarding cities’ livability. The goal of this research was to examine the spatial pattern of temperature across multiple small to medium-sized cities in the state of Iowa, a relatively rural state. The study utilized mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten urban areas in Iowa during the afternoon, evening, and night on days exceeding 32°C from June to September 2022. Using a Random forest algorithm, and independent variables derived from aerial imagery and LiDAR data, we created predicted surface temperature models that demonstrated R² coefficients ranging from 0.879 to 0.997 with a majority of the temperature models reaching a R² higher than 0.95. These strong modeling results and derived highly detailed raster spatial data supports recent research which help explain the influence of morphometric features on temperature patterns in the urban environment. This study expands on previous research by investigating multiple small to medium sized cities in Iowa.

Driven by unusually warm air in the Arctic, severe winter weather moves down south to mid-latitude areas, reflecting the complexity in the ways that climate change may affect local weather extremes. Food and agriculture have been identified as central to global efforts to adapt to climate change. However, research is notably lacking in understanding the vulnerability of farming communities to winter storms that are recognized as one of catastrophic events leading to agricultural damage and loss. This study focuses on mapping the interplay of social and physical factors (e.g. building age, farm-related income, education level, access to farming facilities) that differentiate Iowa farming communities with unequal vulnerability. Semi-structured interview was used in this study to identify the determinants of vulnerability of farming communities to winter storms. Incorporating spatial analysis and factor analysis, this study quantified the effects of physical factors on the sensitivity and exposure of winter storms, and prioritized primary livelihood capitals that determine the adaptive capacity to winter storms. Current results have demonstrated the vulnerability patterns of farming communities to winter storms shaped under varying societal-physical environments and weather conditions. This research shows implications in livelihood pathways to transforming adaptations to vulnerability reduction.

This study examined the potential of integrating fractal texture with spectral information on urban landscape characterization by the maximum likelihood image classifier. The fractal texture was first derived from the red band of a Landsat ETM+ image by applying the triangular prism algorithm at different window sizes. The quality of the twenty-five resultant texture bands were then analyzed by fourteen approaches at both of the pre- and post-classification stages. Results showed all evaluations employed at the pre-classification stage are useful to screen out texture bands more useful than others to facilitate the later supervised image classification. This texture information is observed useful only for classifying medium- and low- density residential categories. The window size leading to the best overall classification accuracy is identified as 31 x 31, implying this scale should be kept as a guideline for future studies if a similar methodology is adopted.

Watershed planning is a pivotal exercise for all jurisdictions irrespective of size, landscape complexity, or other nuances. As a result of the intricate relationship between land use/land cover (LULC) and water resources, it becomes prudent to not only develop historical and contemporary LULC data for watershed planning purposes, but more importantly, the production of future LULC datasets has the potential to better inform watershed planners. This study explored an optimal workflow that can be adopted for the production of baseline LULC input images from a moderate spatial resolution sensor such as Landsat, and the identification, translation, and configuration of land change drivers and regional comprehensive plan prescriptions in the creation of future LULC data for a regional watershed. The study conducted in the Lower Chippewa River Watershed, Wisconsin, USA demonstrated that an object-based hybrid classification approach resulted in the generation of improved projected images with a 15% increase in area under the curve (AUC) value compared to a pixel-based hybrid classification method even though both methods displayed comparable overall image classification accuracies (≤ 1.8%). Results further displayed that configuring anthropogenic drivers in a trend format rather than individual year values can result in a more efficient training of a multi-layer perceptron neural network – Markov Chain model. The calibrated and validated model demonstrated that on average, residential, commercial, institutional, green vegetation/shrub, and industrial LULC are expected to grow through 2050, though at a slower rate (12%) compared to contemporary period (39%), while forest and agricultural lands are slated to decline (−2%).

This study mainly utilized the Soil and Water Assessment Tool (SWAT) together with SWAT-CUP, both free and open source software (FOSS), to construct a distributed hydrologic flow model for the Rokel-Seli River basin, Sierra Leone, in a bid to spatiotemporally evaluate the role of changes in land use/land cover (LULC) and microclimate on streamflow regimes. The model was informed by LULC data derived from three Landsat satellite images collected in 2002, 2010, and 2016. The LULC data was generated with the aid of several python libraries accessed through the FOSS Anaconda Navigator. LULC change analysis demonstrated that between 2002 and 2016, urban, agricultural, water, and mining lands expanded significantly but forest cover reduced the most (−5.7 %). While average annual surface runoff dramatically increased from 2002 to 2010 (31.7 %), the period after the reservoir construction (2010–2016) recorded lower increase in surface runoff (0.9 %). Result of the study suggested that the construction of a major reservoir to support hydroelectricity in concert with significant loss of forest cover and shrub played a greater role in increasing surface runoff compared with the influence of microclimate.

Cytochrome P450 enzyme 2D6 (CYP2D6) is an important member of the cytochrome P450 enzyme superfamily, with more than 100 CYP2D6 allelic variants being previously reported. The aim of this study was to assess the catalytic characteristics of 25 alleles (CYP2D6.1 and 24 CYP2D6 variants) and their effects on the metabolism of propafenone in vitro. Twenty-five CYP2D6 alleles were expressing in 21 Spodoptera frugiperda (Sf) insect cells, and each variant was evaluated using propafenone as the substrate. Reactions were performed at 37 °C with 1–100 μmol/L propafenone for 30 min. After termination, the product 5-OH-propafenone was extracted and used for signal collection by ultra-performance liquid chromatography (UPLC). Compared with wild type CYP2D6.1, the intrinsic clearance (V_max and K_m) values of all variants were significantly altered. Three variants (CYP2D6.87, CYP2D6.90, CYP2D6.F219S) exhibited markedly increased intrinsic clearance values (129% to 165%), whereas 21 variants exhibited significantly decreased values (16% to 85%) due to increased K_m and (or) decreased V_max values. These results indicated that the majority of tested alleles had significantly altered catalytic activity towards propafenone hydroxylation in this expression system. Attention should be paid to subjects carrying these rare alleles when treated with propafenone.