scholarly journals Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States

2020 ◽  
Vol 117 (8) ◽  
pp. 4228-4233 ◽  
Author(s):  
Lin Meng ◽  
Jiafu Mao ◽  
Yuyu Zhou ◽  
Andrew D. Richardson ◽  
Xuhui Lee ◽  
...  
Keyword(s):  
United States ◽  
Rural Areas ◽  
Urban Areas ◽  
Environmental Changes ◽  
Terrestrial Ecosystems ◽  
Urban Heat Islands ◽  
Heat Accumulation ◽  
Spring Phenology ◽  
Heat Islands ◽  
Urban Rural

Urbanization has caused environmental changes, such as urban heat islands (UHIs), that affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Here, we investigated the changes in the satellite-derived start of season (SOS) and the covariation between SOS and temperature (RT) in 85 large cities across the conterminous United States for the period 2001–2014. We found that 1) the SOS came significantly earlier (6.1 ± 6.3 d) in 74 cities and RT was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); 2) the decreased magnitude in RT mainly occurred in cities in relatively cold regions with an annual mean temperature <17.3 °C (e.g., Minnesota, Michigan, and Pennsylvania); and 3) the magnitude of urban−rural difference in both SOS and RT was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in RT magnitude in urban areas. These findings provide observational evidence of a reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in nonurban environments may decline in the warming future.

scholarly journals The Influence of Vegetation on UHIs, MUHIs, and Microclimate of Selected Southern Cities

HortScience ◽  
1997 ◽  
Vol 32 (3) ◽  
pp. 509A-509
Author(s):  
Derald A. Harp ◽  
Edward L. McWilliams
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Building Materials ◽  
Urban Heat Islands ◽  
Shopping Malls ◽  
Heat Islands ◽  
Suburban Areas ◽  
Winter Temperatures ◽  
Urban Heat ◽  
The City

Urban areas have average annual temperatures 2–3°C warmer than surrounding rural areas, with daily differences of 5–6°C common. A suggested reason for this temperature difference is the extensive use of concrete, asphalt, and other building materials in the urban environment. Vegetation can moderate these temperatures by intercepting incoming radiation. The influence of vegetation patterns on the magnitude of urban and micro-urban “heat islands” (UHI and MUHI, respectively) is compared for several cities including Houston, Austin, College Station, and Ft. Worth, Texas; Huntsville, Ala.; and Gainesville, Fla. Temperatures for all cities studied were greatest in the built-up areas and dropped off in suburban areas and adjacent rural areas. In Houston, surrounding rice fields were 3–5°C cooler than urban areas. Heavily built-up areas of Austin were 2–4°C warmer than parks and fields outside of the city. In all of the cities, large parks were typically 2–3°C cooler than adjacent built-up areas. Large shopping malls varied in nocturnal winter and summer temperature, with winter temperatures near door openings 2–3°C warmer, and summer daytime temperatures as much as 17°C cooler beneath trees. This effect seemed to persist at the microclimatic scale. Areas beneath evergreen trees and shrubs were warmer in the winter than surrounding grass covered areas. Video thermography indicated that the lower surfaces of limbs in deciduous trees were warmer than the upper surfaces. Overall, vegetation played a significant role, both at the local and microscale, in temperature moderation.

Automated detection of urban heat islands based on satellite imagery, digital surface models, and a low-cost sensor network                                     

2021 ◽  
Author(s):  
Sebastian Schlögl ◽  
Nico Bader ◽  
Julien Gérard Anet ◽  
Martin Frey ◽  
Curdin Spirig ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Low Cost ◽  
Urban Heat Islands ◽  
Heat Islands ◽  
Micro Scale ◽  
Air Temperatures ◽  
Urban Heat

&lt;p&gt;Today, more than half of the world&amp;#8217;s population lives in urban areas and the proportion is projected to increase further in the near future. The increased number of heatwaves worldwide caused by the anthropogenic climate change may lead to heat stress and significant economic and ecological damages. Therefore, the growth of urban areas in combination with climate change can increase future mortality rates in cities, given that cities are more vulnerable to heatwaves due to the greater heat storage capacity of artificial surfaces towards higher longwave radiation fluxes.&lt;/p&gt;&lt;p&gt;To detect urban heat islands and resolve the micro-scale air temperature field in an urban environment, a low-cost air temperature network, including 450 sensors, was installed in the Swiss cities of Zurich and Basel in 2019 and 2020. These air temperature data, complemented with further official measurement stations, force a statistical air temperature downscaling model for urban environments, which is used operationally to calculate hourly micro-scale air temperatures in 10 m horizontal resolution. In addition to air temperature measurements from the low-cost sensor network, the model is further forced by albedo, NDVI, and NDBI values generated from the polar-orbiting satellite Sentinel-2, land surface temperatures estimated from Landsat-8, and high-resolution digital surface and elevation models.&lt;/p&gt;&lt;p&gt;Urban heat islands (UHI) are processed averaging hourly air temperatures over an entire year for each grid point, and comparing this average to the overall average in rural areas. UHI effects can then be correlated to high-resolution local climate zone maps and other local factors.&lt;/p&gt;&lt;p&gt;Between 60-80 % of the urban area is modeled with an accuracy below 1 K for an hourly time step indicating that the approach may work well in different cities. However, the outcome may depend on the complexity of the cities. The model error decreases rapidly by increasing the number of spatially distributed sensor data used to train the model, from 0 to 70 sensors, and then plateaus with further increases. An accuracy below 1 K can be expected for more than 50 air temperature measurements within the investigated cities and the surrounding rural areas.&amp;#160;&lt;/p&gt;&lt;p&gt;A strong statistical air temperature model coupled with atmospheric boundary layer models (e.g. PALM-4U, MUKLIMO, FITNAH) will aid to generate highly resolved urban heat island prediction maps that help decision-makers to identify local heat islands easier. This will ensure that financial resources will be invested as efficiently as possible in mitigation actions.&lt;/p&gt;

Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts

2013 ◽  
Vol 52 (9) ◽  
pp. 2051-2064 ◽  
Author(s):  
Dan Li ◽  
Elie Bou-Zeid
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Urban Areas ◽  
Heat Waves ◽  
Mitigation Strategies ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Urban Heat ◽  
The Impact

AbstractCities are well known to be hotter than the rural areas that surround them; this phenomenon is called the urban heat island. Heat waves are excessively hot periods during which the air temperatures of both urban and rural areas increase significantly. However, whether urban and rural temperatures respond in the same way to heat waves remains a critical unanswered question. In this study, a combination of observational and modeling analyses indicates synergies between urban heat islands and heat waves. That is, not only do heat waves increase the ambient temperatures, but they also intensify the difference between urban and rural temperatures. As a result, the added heat stress in cities will be even higher than the sum of the background urban heat island effect and the heat wave effect. Results presented here also attribute this added impact of heat waves on urban areas to the lack of surface moisture in urban areas and the low wind speed associated with heat waves. Given that heat waves are projected to become more frequent and that urban populations are substantially increasing, these findings underline the serious heat-related health risks facing urban residents in the twenty-first century. Adaptation and mitigation strategies will require joint efforts to reinvent the city, allowing for more green spaces and lesser disruption of the natural water cycle.

scholarly journals Demographic and Disease Characteristics of People With Multiple Sclerosis Living in Urban and Rural Areas

2006 ◽  
Vol 8 (3) ◽  
pp. 89-97 ◽  
Author(s):  
Robert J. Buchanan ◽  
Randolph Schiffer ◽  
Alexa Stuifbergen ◽  
Li Zhu ◽  
Suojin Wang ◽  
...  
Keyword(s):  
United States ◽  
Multiple Sclerosis ◽  
Rural Areas ◽  
Urban Areas ◽  
The United States ◽  
Primary Progressive ◽  
Disease Characteristics ◽  
Urban Rural ◽  
Disease Modifying ◽  
Urban And Rural Areas

This study compares demographic and disease-related characteristics of people with multiple sclerosis (MS) living in urban and rural areas. The data analyzed for this study were collected from a survey of 1518 people with MS living throughout the United States from October 2004 through January 2005. We found significant urban-rural differences in various MS characteristics, including type of MS. A significantly larger proportion of people with MS in remote rural areas than their urban counterparts responded that they had primary progressive MS. People with MS in rural areas were significantly more likely than those in urban areas to report that MS symptoms interfered with their independence. A significantly larger proportion of people with MS in remote rural areas than in urban areas were not receiving disease-modifying medications. Our results suggest that MS disease expression varies across urban-rural gradients. Although the findings are not definitive, we hope that other investigative groups will build on these results and work toward confirming and understanding them.

scholarly journals IASI observations at the city scale

2020 ◽  
Author(s):  
Sarah Safieddine ◽  
Maya George ◽  
Cathy Clerbaux ◽  
Ana Paracho ◽  
Anne Boynard ◽  
...  
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Spatial Scales ◽  
Urban Heat Islands ◽  
Atmospheric Composition ◽  
Heat Islands ◽  
Large Cities ◽  
Urban Heat ◽  
Good Tracer ◽  
The City

&lt;p&gt;IASI is a versatile mission, allowing the measurement of both meteorological parameters such as temperature and atmospheric composition for infrared absorbing species. With its long observation record and frequent overpasses, IASI is able to follow changes at different spatial scales. We studied IASI&amp;#8217;s capability to track the anthropogenic signature associated with large cities, both in terms of temperature fingerprint (urban heat islands) and carbon monoxide (CO) content, a good tracer of human activity (transport, heating, and industrial activities). For this study we averaged the IASI data available since the launch of the first IASI, in order to increase the signal to noise, and allow discriminating the city from its surroundings.&amp;#160;For skin temperatures we show that some cities experience much warmer temperatures than nearby rural areas, with day and night differences, whereas other urban areas appear as cold urban islands when surrounded by deserts Examples will be shown and compare with MODIS observations. For CO emitted by human activities, we identified some cities that stand out from their background, and were able to compare their CO associated signatures with measurements provided by other available spaceborne instruments such as Mopitt and TROPOMI.&lt;/p&gt;

scholarly journals Thermal Comfort Level Assessment in Urban Area of Petrolina-PE County, Brazil

2017 ◽  
Vol 32 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Pedro Vieira de Azevedo ◽  
Péricles Tadeu da Costa Bezerra ◽  
Mario de Miranda Vilas Boas Ramos Leitão ◽  
Carlos Antonio Costa dos Santos
Keyword(s):  
Thermal Comfort ◽  
Urban Area ◽  
Rural Areas ◽  
Urban Areas ◽  
Thermal Conditions ◽  
Comfort Level ◽  
Urban Heat Islands ◽  
Thermal Discomfort ◽  
Heat Islands ◽  
Urban And Rural Areas

Abstract This study evaluated the thermal conditions of urban areas in Petrolina-PE, from continuous data collected in urban and rural areas for the year of 2012. The results characterized urban heat islands (UHI) with varying intensity in urban areas, especially UHI = 5.3 °C (high intensity) occurred on April 28, 2012. It was evident that the constituent elements of urban areas contribute to the formation and expansion of UHI bringing thermal discomfort for its inhabitants. An adaptation to Thom’s equation for calculating the Thermal Discomfort Index (DIT), was used to obtain the maximum (DITx) and minimum (DITm) thermal discomfort. In the urban area, the DITm indicated thermal comfort in 23.0% of the days and partial comfort in 77.0% of days surveyed. Already, the DITx characterized 71.6% of days with partial comfort and 28.4% of days with thermal discomfort. In the rural area, The DITm indicated that 41.5% of days were thermally comfortable and 58.5% of days had partial comfort. However, the DITx pointed 87.7% of the days of this environment with partial thermal comfort and 12.3% of thermally uncomfortable days. Finally, the results showed that afforestation of urban area constitutes to an effective and efficient way to mitigate thermal discomfort.

scholarly journals Enhanced surface urban heat islands due to divergent urban-rural greening trends

2021 ◽  
Author(s):  
Chi Chen ◽  
Dan Li ◽  
Trevor F. Keenan
Keyword(s):  
Rural Areas ◽  
Urban Heat Islands ◽  
Anthropogenic Heat ◽  
Rapid Urbanization ◽  
Area Index ◽  
Heat Islands ◽  
Urban Rural ◽  
Urban Heat ◽  
Urban And Rural Areas ◽  
The Impact

Abstract Satellite observations show that the surface urban heat island intensity (SUHII) has been increasing over the last two decades. This is often accompanied by an increased urban-rural contrast of vegetation greenness. However, the contribution of uneven vegetation trends in urban and rural areas to the trend of SUHII is unclear, due to the confounding effects of climate change and changes in man-made amenities and anthropogenic heat sources. Here we use a data-model fusion approach to quantify such contributions during the peak growing season. We show that the LAIdif (the urban-rural difference of leaf area index) is increasing (P<0.05) in 189 of the selected 228 global megacities. The increasing trend of LAIdif from 2000 to 2019 accounts for about one quarter of the trend in satellite-derived SUHII, and the impact is particularly evident in places with rapid urbanization and rural cropland intensification. The marginal sensitivity of SUHII to LAIdif is the strongest in hot-humid megacities surrounded by croplands and in hot-dry megacities surrounded by mixed woody and herbaceous vegetation. Our study highlights the role of long-term vegetation trends in modulating the trends of urban-rural temperature differences.

scholarly journals Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)

2021 ◽  
Vol 13 (16) ◽  
pp. 3177
Author(s):  
Talha Hassan ◽  
Jiahua Zhang ◽  
Foyez Ahmed Prodhan ◽  
Til Prasad Pangali Sharma ◽  
Barjeece Bashir
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Thermal Environment ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Urban Heat

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

Urban–Rural Dichotomy in Planning: Review of Legal Framework in Four States of India

2021 ◽  
pp. 001955612110016
Author(s):  
Anurima Mukherjee Basu ◽  
Rutool Sharma
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Planning Process ◽  
Legal Framework ◽  
Quantum Jump ◽  
Class 1 ◽  
Urban Settlements ◽  
Urban Rural ◽  
Territorial Approach ◽  
Sizeable Number

Current urbanisation trends in India show a quantum jump in number of ‘census towns’, which are not statutorily declared as urban areas, but have acquired all characteristics of urban settlements. Sizeable number of such census towns are not located near any Class 1 city. Lack of proper and timely planning has led to unplanned growth of these settlements. This article is based on a review of planning legislations, institutional framework and planning process of four states in India. The present article analyses the scope and limitations of the planning process adopted in the rapidly urbanising rural areas of these states. The findings reveal that states are still following a conventional approach to planning that treats ‘urban’ and ‘rural’ as separate categories and highlights the need for adopting an integrated territorial approach to planning of settlements.

scholarly journals Urban–rural disparity in cancer incidence in China, 2008–2012: a cross-sectional analysis of data from 36 cancer registers

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e042762
Author(s):  
Shuai Yuan ◽  
Shao-Hua Xie
Keyword(s):  
Cancer Incidence ◽  
Rural Areas ◽  
Urban Areas ◽  
Incidence Rates ◽  
Cancer Type ◽  
Anatomic Site ◽  
Cross Sectional ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Urban Rural

ObjectiveThe substantial differences in socioeconomic and lifestyle exposures between urban and rural areas in China may lead to urban–rural disparity in cancer risk. This study aimed to assess the urban–rural disparity in cancer incidence in China.MethodsUsing data from 36 regional cancer registries in China in 2008–2012, we compared the age-standardised incidence rates of cancer by sex and anatomic site between rural and urban areas. We calculated the rate difference and rate ratio comparing rates in rural versus urban areas by sex and cancer type.ResultsThe incidence rate of all cancers in women was slightly lower in rural areas than in urban areas, but the total cancer rate in men was higher in rural areas than in urban areas. The incidence rates in women were higher in rural areas than in urban areas for cancers of the oesophagus, stomach, and liver and biliary passages, but lower for cancers of thyroid and breast. Men residing in rural areas had higher incidence rates for cancers of the oesophagus, stomach, and liver and biliary passages, but lower rates for prostate cancer, lip, oral cavity and pharynx cancer, and colorectal cancer.ConclusionsOur findings suggest substantial urban–rural disparity in cancer incidence in China, which varies across cancer types and the sexes. Cancer prevention strategies should be tailored for common cancers in rural and urban areas.

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