Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru, India

Rapid urbanization and meteorological changes are reshaping the urban vegetation pattern in urban core area: A national 315-city study in China

Urban vegetation takes on the responsibility of improving the urban environment and human wellbeing. However, the changing pattern and its driving mechanism are still not well understood at the national scale, especially in China under nearly 20 years-long rapid urbanization. In this study, for urban core area in 315 cities, over 18,000 high-resolution remote sensing images across 18 years were used to detect the spatiotemporal changes of urban vegetation and furtherly explore the interaction and independence of rapid urbanization and meteorological change. We found that, urban vegetation coverage decreased from 12.23 % to 5.91 % (-0.35 % per year) in 2003 to 2020. Urban vegetation per capita presented a steeper decline by 68 % (-0.51 m2 per capita per year) from 18.94 m2 in 2003 to 9.83 m2 in 2020. Spatially, the northwest and central-south zone decreased faster at the regional scale, and small cities contribute the higher decreasing rate. From 2003 to 2020, urbanization is the significant negative factor which contribute to 29.6 % of the reduction, and the meteorological factors do not affect urban vegetation change. Also, we found that the temporal pattern of urban vegetation change could be separated into two stages, including a rapid decline stage (2009-2020) and a progressively declining stage (2003-2008), each has its own driving mechanism. From 2003 to 2008, the decline in urban vegetation had insignificant relationship with meteorological changes and rapid urbanization. However, from 2009 to 2020, urbanization became the most critical factor to affect the urban vegetation, the contribution of urbanization rises to 30.3 %, meteorological factors contribute 14.3 % to the variation (r2 = 0.52). A growing crisis awareness of the rapid decline (especially in 2009 to 2020) of urban vegetation should return to the public scene, and these findings may provide some essential suggestions for securing this urban ecological barrier.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

A modelling study on quantifying the impact of urbanization and regional effects on the wintertime surface temperature over a rapidly-growing tropical city

Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.

Double Effect of Urbanization on Vegetation Growth in China’s 35 Cities during 2000–2020

In recent decades, the trade-off between urbanization and vegetation dynamics has broken the balance between human activities and social-economic dimensions. Our understanding towards the complex human–nature interactions, particularly the gradient of vegetation growth pattern across different city size, is still limited. Here, we selected 35 typical cities in China and classified them into five categories according to their resident population (e.g., megacities, megapolis, big cities, medium cities, and small cities). The spatial-temporal dynamics of vegetation growth for all 35 cities were inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). We found that averaged NDVI for all cities slightly decreased during 2000 and 2020, at a rate of 1.6 × 10−4 per year. Most cities were characterized with relatively lower NDVI in urban areas than its surrounding area (determined by a series of buffer zones, i.e., 1–25 km outside of the city boundary). The percentage of greening pixels increased from urban area to the 25 km buffer zone at a rate of 4.7 × 10−4 per km. We noticed that negative impact of urbanization on vegetation growth reduced as the distance to urban area increased, with an exception for megacities (e.g., Shanghai, Beijing, and Shenzhen). In megacities and megapolis, greening pixels were more concentrated at core urban area, implying that the positive urbanization effect on vegetation growth is much more apparent. We argue that urbanization in China might facilitate vegetation growth to a certain extent, for which an appropriate urban planning such as purposeful selection of city sizes could be a scientific guidance while targeting the city’s sustainable development goals in future.

Spatiotemporal Influences of LULC Changes on Land Surface Temperature in Rapid Urbanization Area by Using Landsat-TM and TIRS Images

The inverse correlation between NDVI and LST is widely known for its long time series. However, when more specific statistical tests were performed, subtle differences in the correlation behavior over time are more clearly observed. In this work, regression analyses were performed between NDVI and LST at intervals of approximately 10 years, quantifying this relationship for an area of transition from vegetation to urban occupation from 1985 to 2018. The removal of vegetation cover (reduction of 51% to 7% in grassland and 14.4% to 0.6% in forest) to occupy impermeable surfaces ( increase of 31% to 91% in urban areas) caused an average LST increase of 4.18 °C when compared to the first and last decades of the historical series. Temporal analysis allowed us to verify the increase in temperature in the four seasons. The largest difference was 6.36 °C between the first and last decade of autumn, 4.40 °C in spring, 4.09 °C in summer, and 2.41 °C in winter. The results also show that LST has a negative correlation with NDVI, especially in urban areas, with an increase in this correlation during the period (1989: R = −0.55; 1999: R = −0.58; 2008: R = −0.59; 2018: R = −0.76). Our study results will help policymakers understand the dynamics of temperature increases by adding scientifically relevant information on the sustainable organization of the urban environment.

The spatiotemporal response of China's vegetation greenness to human socio-economic activities

Climate change and human socioeconomic activities both strongly impact long-term vegetation greenness. It is more a challenge to evaluate the impacts of socioeconomic activities on vegetative greenness than climate change, partially due to the lack of appropriate quantitative indicators of the former. Here we examined the relationship between the remote sensing nighttime light (NTL) data and the Normalized Difference Vegetation Index (NDVI), which in this study are used as the proxies of socioeconomic activities and vegetation greenness, respectively. We first eliminated the vegetation greenness changes in response to climate change and calculated the human-activities-induced NDVI (HNDVI). After explored the spatiotemporal patterns of the HNDVI and NTL data across China from 1998 to 2018, we studied the relationship between the HNDVI and NTL at the grid and county levels, respectively. Our results show that the mean adjusted DN values of the NTL data (NTLI) continuously increase (+0.2938) across our study area from 1998 to 2018, whereas the HNDVI values fluctuate with a general upward trend (+0.0018). Most grids (91.2%) with increased HNDVI were found in rural areas, particularly in the Northeast forest shelterbelt and the Loess Plateau. By contrast, the HNDVI values in rapidly urbanized areas in Chinese major urban agglomerations mainly show a downward trend, especially in the Yangtze River Delta (YRD) urban agglomeration. The relationships between the NTLI and HNDVI are inconsistent over time and across space, which could be attributed to land use conditions, afforestation projects in rural areas, and greening activities in urban areas over different periods and regions.

Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Variability of ozone and oxides of nitrogen in the tropical city, Bengaluru, India

Bengaluru, also considered India's Silicon Valley, has seen steady growth in population over the years. Bengaluru's rapid development has resulted in dwindling reservoirs, increased traffic congestion, high levels of air pollution, and, to some measure, a rise in summer temperatures. As a result of these changes in urban form over the last decade, anthropogenic heat fluxes for ozone production have increased. However, an observational study on the effects of growing urbanisation on trace gases in Bengaluru for various seasons and periods of the day is missing. Hence, in situ measurements of O₃, NO, NO₂, and NO_X concentrations were carried out at Bengaluru, India, from January 2015 to December 2018. The data were examined for diurnal and interannual variations in trace gas mixing concentrations. The diurnal trend in O₃ exhibits unimodal behaviour. Changes in photochemistry, local meteorology, and the planetary boundary layer's distinctive features cause a rise in the value of concentrations and lead to a peak. In contrast, the diurnal trend in NO, NO₂, and NO_X displayed a bimodal peak due to the combined effect of vehicular emissions and the planetary boundary layer. The link involving the oxidant OX (O₃ + NO₂) and NOx levels were investigated to determine the NOx-independent regional and NOx-dependent local contributions to OX in the atmosphere. Daytime contributions are higher than night-time contributions, according to the present study. The observed anomalies could be the consequence of photochemical processes that produce OX.

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus and Web of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.

COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India

The COVID-19 pandemic forced a nationwide lockdown in India for months when close to 1.3 billion people were confined to their homes. An abrupt halt in the majority of the urban activities reduced the generation of anthropogenic heat which often exacerbates the Urban Heat Island (UHI) effect in the urban pockets of the country. We studied the lockdown impact on seven highly populated and polluted mega urban agglomerations across India, namely Delhi, Ahmedabad, Hyderabad, Kolkata, Mumbai, Bengaluru and Chennai, using near-anniversary Landsat 8 data. The results revealed that the lockdowns have improved the air quality and reduced the Land Surface Temperature (LST) and hence the UHI effect over these cities. Each of the cities experienced an improved Air Quality Index (AQI) ranging from 18 to 151 units except Chennai (with a marginal 8 units increase in AQI), a decrease in mean LST in the range of 0.27 °C to 7.06 °C except Kolkata which showed an increment by ∼4 °C, and a reduction in daily averaged air temperature ranging from 0.3 °C to 10.88 °C except Hyderabad which witnessed an increase of 0.09 °C during the lockdown (April 2020) compared to the previous years (April 2019 and 2018). Delhi exhibited the maximum positive impact of the lockdown in all aspects with two-fold improved air quality, and Ahmedabad showed the least improvement. In addition to the variations in regional land use and land cover and proportion of essential industries that remained operational throughout the lockdown, the geographic location, topography, local meteorology and climate were some of the other factors also responsible for either aiding or overcompensating the large scale LST variabilities observed in these cities. These results hint at an unprecedented opportunity to evaluate the effectiveness of periodic planned lockdowns as a possible mitigating measure to reduce LST spikes and degraded air quality in urban areas in the future.

Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India

The emergence of COVID-19 has brought a serious global public health threats especially for most of the cities across the world even in India more than 50 % of the total cases were reported from large ten cities. Kolkata Megacity became one of the major COVID-19 hotspot cities in India. Living environment deprivation is one of the significant risk factor of infectious diseases transmissions like COVID-19. The paper aims to examine the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. COVID-19 hotspot maps were prepared using Getis-Ord-Gi* statistic and index of multiple deprivations (IMD) across the wards were assessed using Geographically Weighted Principal Component Analysis (GWPCA).Five count data regression models such as Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR) were used to understand the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. The findings of the study revealed that living environment deprivation was an important determinant of spatial clustering of COVID-19 hotspots in Kolkata megacity and zero-inflated negative binomial regression (ZINBR) better explains this relationship with highest variations (adj. R2: 71.3 %) and lowest BIC and AIC as compared to the others.

Advances in Modelling and Prediction on the Impact of Human Activities and Extreme Events on Environments

Fast urbanization and industrialization have progressively caused severe impacts on mountainous, river, and coastal environments, and have increased the risks for people living in these areas. Human activities have changed ecosystems hence it is important to determine ways to predict these consequences to enable the preservation and restoration of these key areas. Furthermore, extreme events attributed to climate change are becoming more frequent, aggravating the entire scenario and introducing ulterior uncertainties on the accurate and efficient management of these areas to protect the environment as well as the health and safety of people. In actual fact, climate change is altering rain patterns and causing extreme heat, as well as inducing other weather mutations. All these lead to more frequent natural disasters such as flood events, erosions, and the contamination and spreading of pollutants. Therefore, efforts need to be devoted to investigate the underlying causes, and to identify feasible mitigation and adaptation strategies to reduce negative impacts on both the environment and citizens. To contribute towards this aim, the selected papers in this Special Issue covered a wide range of issues that are mainly relevant to: (i) the numerical and experimental characterization of complex flow conditions under specific circumstances induced by the natural hazards; (ii) the effect of climate change on the hydrological processes in mountainous, river, and coastal environments, (iii) the protection of ecosystems and the restoration of areas damaged by the effects of climate change and human activities.