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Environmental problems are the major concern for most of the metropolitan cities of the world now days where better employment opportunities in industrial and service sector with better transport connectivity attracts immigration which put stress on the fringe areas. This urbanization process takes place in surrounding the city and with great pace along the transport corridors, in the proximity of which land use / land cover change occur. It gives rise to many environmental problems such as air pollution, water pollution and depletion together with land degradation etc. Among these environmental problems, Urban Heat Island (UHI) effect is one of the major one in metro cities in general and at mega cities in particular. UHI is not only the result of a single factor but it is a complex phenomenon which determines microclimate of any city. It starts with solar radiation as different land uses have different albedo and retention capacity, which are responsible for this effect. Apart from these other anthropogenic factors such as transportation and industrial pollution with thermal power plants emission also contributes in UHI intensification. UHI raise pollution level which may affect biodiversity negatively as extinction of plants and animals may occur.

Extensive Literature Survey has been conducted for the present theme of research. Kumar and Singh (2003) studied climate change in Metropolitan cities where they took land use as the most important indicator for determining the process of urbanization. Doughlas (1983) studied land use pattern and trend in metropolis cities and conclude that as city grows its pace accelerate and bigger the city the more demand on the surrounding countryside and it lead to the greater danger of damaging the natural environment of surrounding as cultivable agricultural land or green cover comes under the built up area. Oke (1973) stated that land is most dominant factor in UHI intensification and it intensifies as city grows. There is not any standard yet accepted unanimously about collection of surface temperature. So there are many proxy ways of getting surface temperature through remote sensing and land use study.  The warming at the surface is not supported by balloon mounted pressure transducers of Pielke et al. (1998) or by satellite observations Christy et al. (2003).

There are problems with the surface thermometer measurements. Since the satellite observations are validated by the balloon observations, they are the best temperature measurements available. There are many things wrong with the surface network such as poor geographical coverage with less than 30 per cent of the globe having sensors (Santer et al., 2000), urban heat island contamination (Oke, 1973), land use changes (Marland et al., 2003; Kalnay and Cai, 2003). In last fifty years mean surface temperature in United States has increased 0.27⁰ C due to land use changes. These problems with the surface thermometers lead to measurements of greater warming than is actually occurring compared to a situation where we had perfect measurements. For example Kalnay and Cai (2003) estimates 40 per cent of the surface warming is coming from land use changes. Marland et al. (2003) said that more than half the warming may be coming from these land use changes.. UHI affected cities has 5 degree Celcius warmer centre than hinterland in general but it may be 10degree -14degree Celcius Terjung et al. (1973) skyscrapers can absorb more than six times energy than rural while suburbs absorb slightly more than rural.

Mikami et al. (2002) used surface temperature equivalent (TRF) and affirmed that heat capacity and thermal conductivity of central city area is two to four times larger than surroundings The National Capital Territory of Delhi covers an area of 1,483 sq. km with 51.9 kms of length and 48.5 kms of width. It is situated between the Himalaya  and the Aravalli range. It is surrounded on three sides by Haryana and to the east, across the Yamuna by Uttar Pradesh. It has extreme climate due to its continental situation which is very cold in winter and terribly hot in summer. It is called land locked city because it is situated between the Himalaya in the north, central hot plain in the south, the Thar Desert of Rajasthan to the west and the Ganga plain in the east. It divides Ambala plain and Varanasi plain also. Being the National capital it is nucleus of trade, commerce and industry in northern India. Basically, it is known as service town but industrial, educational and commercial activities are also equally important. Land degradation plays a decisive role in influencing the status of human health by altering land use patterns and offering a favorable milieu for illness vectors such as malaria and diarrhea. Degraded land is defined as ‘land which due to natural processes or from human activity is no longer able to sustain economic function and the original natural ecological function’ (FAO, 1998:31)

Database and Methodology

The ambient air temperature pattern and Land surface temperature pattern has been analysed using  Indian meteorological Department data provided by Data Dissemination Centre Pune India, Census of India and United States Geological Survey (USGS Landsat Series 5 and 8). Microsoft Excel has been used to analyses the IMD data and ArcGIS along with QGIS have been used for USGS Landsat Data analysis.  The Following Formula has been used to derive LST with the help of radiance and Brightness value derivation.

Lλ = ML * Qcal + AL

where: Lλ = Spectral radiance (W/(m2 * sr * μm)) ML = Radiance multiplicative scaling factor for the band (RADIANCE_MULT_BAND_n from the metadata) AL = Radiance additive scaling factor for the band (RADIANCE_ADD_BAND_n from the metadata) Qcal = Level 1 pixel value in DN. The following equation is used to convert Level 1 DN values to TOA reflectance: ρλ’ = Mρ * Qcal + Aρ where: ρλ' = TOA Planetary Spectral Reflectance, without correction for solar angle. (Unitless) Mρ = Reflectance multiplicative scaling factor for the band (REFLECTANCEW_MULT_BAND_n from the metadata). Aρ = Reflectance additive scaling factor for the band (REFLECTANCE_ADD_BAND_N from the metadata). Qcal = Level 1 pixel value in DN. TIRS data has converted from spectral radiance (as described above) to brightness temperature, which is the effective temperature viewed by the satellite under an assumption of unity emissivity. The conversion formula is as follows: where: T = Top of atmosphere brightness temperature (K) where: Lλ = TOA spectral radiance (Watts/(m2 * srad * μm)) K1 = Band-specific thermal conversion constant from the metadata (K1_CONSTANT_BAND_x, where x is the thermal band number) K2 = Band-specific thermal conversion constant from the metadata (K2_CONSTANT_BAND_x, where x is the thermal band number)

 

Study Area

Delhi has tropical steppe climate with extremely hot summers and moderately cold winters. Only during the monsoon period (July to September) air of oceanic origin penetrate to Delhi and increase humidity, cloudiness and precipitation. The cold season starts in late November and extends to about the beginning of March. This is followed by the hot season which lasts till about the end of June when the monsoon arrives. The total mean annual rainfall is 715 mm Maximum rainfall occurs in July (211 mm). The monsoon continues to the last week of September. The post monsoon (October and November) constitute a transition period from the monsoon to winter conditions (IMD, 1991).  The weather condition of the Delhi is influenced by the inland position with the great desert of Rajasthan to west and South west and The Gangetic plain of Uttar Pradesh to the east. Extreme dryness with an intensely hot summer and cold winter are the characteristic feature of the weather.

Delhi is terribly hot in summer and chilling cold in winter. In summer maximum temperature has recorded 48.400 C on 26 May 1998 while minimum in winter was -2.200 C on 11January 1967(IMD). Although May has maximum daytime temperature but June is the hottest month in Delhi when minimum temperature does not fall greatly even in nights. Generally higher temperature is concentrated in the central part of city while it decreases outwards. It is more temperature recorded where population density is high. Apart it temperature is higher along industrial areas also.

Delhi has been experiencing higher growth rate than surroundings since centuries but in recent decades it is quite higher.

Results and Discussions

The whole Spatio-temporal Land Surface Temperature (LST) Analysis study is based on the ancient proverb that a picture is worth than thousand of words so the Pictorial representation in the form of map is worth here to show the Climatic variability in Micro Climate of Delhi in terms of urban heat island effect clearly visible in the form of Land Surface Temperature showing increasing trend. The LST analysis is based upon the USGS data and it is found that Land Surface Temperature (LST) ranges between about 24 to 36 degree in 1992 pre-monsoon period while it ranges between 32 – 42 degree Celsius in 2021.  It is clearly evident that Land surface temperature has risen at an unprecedented rate

Fig. 1: LST Pre-monsoon 1992

 

Fig. 2: LST Pre-monsoon 2021

 Fig. 3: LST Post-monsoon 1992

Fig. 4: LST Post-monsoon 2021
It is evident clear with the above maps (Fig 1-4) and data given below in Table 1 and 2 that drastic changes has occurred in terms of temperature change in NCT of Delhi within a short span of time (climatologically) of about 30 years  is an alarming sign in near future.

Table 1: Land Surface Temperature Pre-monsoon

Delhi (01 April 1992)
Temperature	Percent Area
<24	19.14
24-28	54.76
28-32	24.54
32-36	1.52
> 36	0.04

Table 2. Land Surface Temperature Pre-monsoon

Delhi (01 April 2021)
Temperature	Percent Area
<32	12.49
32-35	51.18
35-38	32.55
38-41	3.69
>41	0.09

Trend Analysis (1965-2004)

Trend Analysis for the two major IMD stations of New Delhi named Palam and Safdarjung have been selected for the present theme of research. There is an assumption that at least 30-35 year data must be analyses to study about any climatic variability so 1965-2004 data has analyzed using SPSS and Microsoft Excel.

Mean Maximum Temperature has chosen as an Indicator to find the Climatic variability because Urban areas has the major problem of Urban Heat Island which produces discomfort zone and health issues.

Month wise Analysis has done for the both of stations and it is found that for the month of January there is decline in the mean monthly Temperature of both the stations with slight variability in 1968 it was 18.8 and 19.2 for Palam and Safdarjung Respectively and 19.3 degree celcius for both the stations in 1975. There is no such trend has been observed for the month of February and March Also. In the month of April Slight upward trend has been observed with almost stagnant May and June month. After 1985 July has been showing little upward trend with slight variations. In August and September month also there is no such trend observed. In Post Monsoon Period October overall Slight Downward trend observed and November month has the maximum Variability with continuous ups and downs year wise. December also depicts no trend except decline in 1998.

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