Sudden Climate Change Research Articles

Statistical Modelling and Projection of Future Rainfall using SARIMA and Hybrid SARIMA-GARCH Models in Various Zones of Kerala

Water is an important natural resource considered as basic need for all living things around the world. The volume of pure water present in the Earth is regulated by the amount of rainfall received over the years. Sudden climatic changes are observed in throughout the world which led to flood, drought and uneven rainfall over the years. In this study, SARIMA and SARIMA-GARCH models are applied for forecasting rainfall in different zones of Kerala. The presence of heteroscedasticity in residuals obtained from SARIMA model was identified using ARCH-LM test and it was eliminated by applying SARIMA-GARCH model to the same. The ARCH-LM test results confirmed the presence of heteroscedasticity in residuals. The comparison of models used for predicting rainfall revealed that hybrid SARIMA-GARCH model is more efficient in projecting future values of rainfall in the northern and southern zones of Kerala whereas SARIMA model is showing more accuracy in the central zone of Kerala even in the presence of heteroscedasticity of residuals. The comparison of rainfall forecasted in different zones of Kerala clearly indicated that rainfall is higher in the northern zone whereas lower in the southern zone. In the northern and central zones, the rainfall showed a peak from June to September and almost negligible rainfall from December to February. The outperformed model in each zones of Kerala was applied for projection of future rainfall for next 5 years (2021-2025). Compare to previous years, the rainfall in the northern and central zones is expected to decrease whereas in southern zone of Kerala, rainfall will be almost same.

Stability of a stochastic brucellosis model with semi-Markovian switching and diffusion.

To explore the influence of state changes on brucellosis, a stochastic brucellosis model with semi-Markovian switchings and diffusion is proposed in this paper. When there is no switching, we introduce a critical value and obtain the exponential stability in mean square when by using the stochastic Lyapunov function method. Sudden climate changes can drive changes in transmission rate of brucellosis, which can be modelled by a semi-Markov process. We study the influence of stationary distribution of semi-Markov process on extinction of brucellosis in switching environment including both stable states, during which brucellosis dies out, and unstable states, during which brucellosis persists. The results show that increasing the frequencies and average dwell times in stable states to certain extent can ensure the extinction of brucellosis. Finally, numerical simulations are given to illustrate the analytical results. We also suggest that herdsmen should reduce the densities of animal habitation to decrease the contact rate, increase slaughter rate of animals and apply disinfection measures to kill brucella.

Growth Performance of Kappaphycus alvarezii and Kappaphycus striatus cultivated in Ekas Bay, East Lombok Regency, West Nusa Tenggara Province

alvarezii is a superior product of Indonesian fisheries, producing carrageenan which is used as a food, cosmetic, and medicinal ingredient. K. striatus is a carrageenan-producing red alga that began to be cultivated in the Ekas waters of East Lombok in 2023. K. striatus is more resistant to epiphytes and sudden climate change. The purpose of this study was to analyze the growth of Kappaphycus alvarezii and Kappaphycus striatus. The research was conducted in Ekas Bay, Jerowaru District, East Lombok Regency. The research was conducted from November 8, 2023, to January 8, 2024. This research used an experimental method with T-test analysis. Treatments in this research are the cultivation of K. alvarezii and K. striatus. Cultivation was carried out at Ekas Bay. Analysis of seaweed carrageenan was carried out in the Laboratory of Fish Production and Reproduction, Aquaculture Study Program, Faculty of Agriculture, Mataram University. The result of this research is K. striatus has a higher absolute weight of 154 g compared to K. alvarezii seaweed which is 72 g. The specific growth rate K. striatus higher at 2,98 % / day compared to K. alvarezii is 1, 98 % / day. The yield of carrageenan of K. alvarezii is 1,95% and K. striatus 0,88%. Antioxidant of K. alvarezii is 5,46 % and K. striatus is 7,42%. Chlorophyll K. alvarezii is 5,77 mg/l and K. striatus is 3,51 mg/l. The conclusion of this study is the growth of K. striatus is faster than K. alvarezii.

Making Solar PV Street Light – A New Convention

Today’s society is completely based on fossil fuels directly or indirectly for every aspect of its existence. The transformation from fossil fuels to green energy is essential for coming decade to mitigate sudden climate change and most importantly to ensure long term sustainability. Though this shifting works well in theory, but the reality is that shifting from fossil fuels is not so easy as it is considered to be most available convenient source of energy. Sustainable solution with modern creative ideas embedded with traditional practices, can reduce the dependency on fossil fuels on everything from energy to infrastructure. Today Light emitting diode (LED) lamps have replaced High intensity discharge (HID) lamps that were used at urban street light. Solar Street lights work on the principle of photovoltaic cell or solar cell. This paper presents an overview of the design and implementation of the solar PV street lights as a new convention in outdoor lighting technology. Solar PV Street lights eliminate the need of grid electricity, making it cost effective and environment friendly in urban and rural areas. The system’s components include solar panel, batteries, light sources, charge controllers and a pole structure. In short, this paper is based on the idea of maintaining the maximum utilization and minimum loss of available energy and also making an approach to increase the longevity of the system’s components with some common techniques and make a shift from conventional to non-conventional source of energy and thus a new convention is proposed.

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

AbstractThe escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Impact of Climate Change on Infrastructure Resilience and Adaption Strategies

Infrastructure networks have an severe effect by the physical impacts of climate mutability and change, but will also play an crucial role in building resilience to those impacts. Furthermost events explains the extensiveness of this potential exposure. For example, OECD [Organization For Economic And Development ] modelling of the potential impacts of a major flood in Paris found that 30% to 55% of the direct flood damages would be tolerated by the infrastructure sector, while 35% to 85% of business losses were caused by disturbance to the transportation and electricity supply and not by the flood itself. Climate change has very diverse effect on the infrastructure. If the infrastructure will be climate resilient will help to reduce direct losses and reduce the indirect costs of disturbance created by the sudden climate change. New infrastructure should be hierarchic, planned, designed, built and set off in such a way to resist the climate changes that may occur over anytime during their lifetimes. The old Existing infrastructure may need to be rebuilt , or managed differently according to the given climate change. Lastly, additional infrastructure, such as sea walls, will need to be constructed to address the physical impacts of climate change. This additional infrastructure should or may be include traditional infrastructure, such as hard safeguarding and other engineered solutions, as well as natural infrastructure, such as wetlands and other naturally-based solutions. A wide range of actors, both in the public and private sectors, are taking action to strengthen climate resilience. This report highlights appears good practices and remaining challenges across OECD[Organization For Economic And Development] and G20 countries. It provides non-prescriptive guidance to countries as they find the way to enhance resilience in line with their national circumstances and priorities. Key Words: Infrastructures , Climate changes , Resilience , OECD , Challenges , Impacts , Design , Sustainable , Adaptation , Strategies .

Pandemics in Roman Empire correlate with sudden climate changes

A new temperature and precipitation proxy record shows that periods of rapid cooling align with the civilization’s three worst disease outbreaks.

머신러닝 기반 노지 환경 변수에 따른 예측 토양 수분에 미치는 영향에 대한 연구

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

Climate is changing; Temperature and Rainfall Pattern (Variability) in Jos-Metropolis, Plateau state, Nigeria

Temperature and Rainfall are the most important parameters in determining climatic change in any given environment. This study investigate temperature and rainfall pattern in Josmetropolis over a given 30years (1989-2018) instruments records. Experience of sudden climatic change in temperature from lower reading of temperature to high temperature as well as in rainfall necessitated the study to observe the variability in the parameters. Descriptive method was implored in the research of which data were collected from synoptic station of Nigerian Meteorological Agency (NIMET), Heipang Jos-Plateau from 1989-2018 of temperature and rainfall. A line graph was used for the presentation of data and analytically analyzed using the Pearson Correlation Coefficient (PCC) at 0.05% confidence level. The findings revealed that irregularities temperatures and rainfall which brought about climate variation the area. There was sharp increase in 2010 with a stable high temperature from 2010-2018 while rainfall recorded high rainfall in 1993, 1999 and 2018. Thus, there was no relationship between average temperature and average rainfall but strong relationship between periods of years and temperature as well as rainfall at 0.05%. Furthermore, stipulated recommendations were offered to stakeholders in Jos-Metropolis.

Advanced detection of fungi-bacterial diseases in plants using modified deep neural network and DSURF

Food is indispensable for humans as their growth and survival depend on it. But nowadays, crop is getting spoiled due to fungi and bacteria as soil temperature are changes very rapidly according to sudden climate changes. Due to fungi-bacterial crop, the quality of food is declining day by day and this is really not good for human health. The goal of this research paper is the advanced detection of fungi-bacterial diseases in plants using modified deep neural network approach and DSURF method in order to enhance the detection process. Proposed approach of this research is to use the artificial intelligence techniques like neural network model and dynamic SURF method in order to identify and classify the plant diseases for fungus and bacteria. Additionally, support dynamic feature extraction DSURF & classifier combinations for creating image clusters with the help of Clustering. Deep learning model is employed for training and testing the classifier. The quantitative experimental results of this research work are claimed that authors have achieved the 99.5% overall accuracy by implementing DNNM and DSURF which is much higher than other previous proposed methods in this field. This proposed work is a step towards finding the best practices to detect plant diseases from any bacterial and fungal infection so that humans can get healthy food.

Identifying strategies for adaptation of rural women to climate variability in water scarce areas

The current research aims to identify adaptation strategies adopted by rural women of Fars province in dealing with climate variability. This study is applied in terms of purpose, descriptive-correlational in terms of methodology, and field type in terms of data collection. The statistical population were rural women who were members of agricultural households in Fars province, which have been affected by climatic variability in four climatic zones: cold, mild, hot, and very hot. This study was conducted using survey method. A structured questionnaire was designed and its face and content validities were verified using experts' opinions. The size of the studied sample was estimated using Cochran's formula. For sampling, a combined stratified and cluster sampling method was used. Data were collected from 406 respondents from 32 villages in eight counties of Fars province. Then, the data was analyzed. In total, 43 adaptation strategies of rural women were classified in the form of four categories of strategies for development and diversification of income sources, cost management, social capital management, and environmental stress management. The most important adaptation strategy used by rural women against climatic changes has been the cost management strategy. The use of facilities and loans is the most important strategy among the cost management strategies that can lead to reducing the negative effects of climate variability on rural women. Then, the strategy of development and diversification of income sources, management of social capital, and management of environmental tensions were placed in the second to fourth places of adaptation strategies used by rural women under climate variability. The results of this study indicate that the rural women of Fars province are not only passive victims of climate changes, but they act actively and hope to adapt to sudden climate changes. Using prudent strategies, they employ their experience and expertise to reduce the adverse effects of climate variability.

Current Breeding Approaches in Pearl Millet to Enhance the Nutritional Quality

Pearl millet is an important millet crop, as it is a powerhouse of nutrition with capability to grow at harsh climatic conditions. It is able to overcome sudden climate changes and other natural disasters which can create food security problem by raising the price of foods there by reduce the availability of food materials. Pearl millet can be an alternative nutritious crop for the poor men which provide enough nutrition for active and healthy life. It is cheap source of nutrition when compared to other major cereal crops. While having its nutrition and health benefits, utilization of this crop is restricted due to lack of knowledge and poor keeping quality.
 Different breeding strategies like biofortification breeding, making of synthetic and composites, hybridization techniques by using A,B,R lines, Genomics, Speed breeding are frequently utilized in worldwide. Different improved lines with enhanced zinc and iron content may be used under hybridization programme to overcome the problem of low zinc and iron varieties. Therefore, the aim of this review is to provide the information about nutritional profile, health benefits, biofortification process, current breeding approaches and future prospects.

Masking preprocessing in transfer learning for damage building detection

The sudden climate change occurring in different places in the world has made disasters more unpredictable than before. In addition, responses are often late due to manual processes that have to be performed by experts. Consequently, major advances in computer vision (CV) have prompted researchers to develop smart models to help these experts. We need a strong image representation model, but at the same time, we also need to prepare for a deep learning environment at a low cost. This research attempts to develop transfer learning models using low-cost masking pre-processing in the experimental building damage (xBD) dataset, a large-scale dataset for advancing building damage assessment. The dataset includes eight types of disasters located in fifteen different countries and spans thousands of square kilometers of satellite images. The models are based on U-Net, i.e., AlexNet, visual geometry group (VGG)-16, and ResNet-34. Our experiments show that ResNet-34 is the best with an F1 score of 71.93%, and an intersection over union (IoU) of 66.72%. The models are built on a resolution of 1,024 pixels and use only first-tier images compared to the state-of-the-art baseline. For future orientations, we believe that the approach we propose could be beneficial to improve the efficiency of deep learning training.

Analysis of development in solar greenhouses

The move towards a de-carbonised world, driven partly by climate science and partly by the business opportunities it offers, will need the promotion of environmentally friendly alternatives, if an acceptable stabilisation level of atmospheric carbon dioxide is to be achieved. This requires the harnessing and use of natural resources that produce no air pollution or greenhouse gases and provides comfortable coexistence of human, livestock, and plants. This study reviews the energy-using technologies based on natural resources, which are available to and applicable in the farming industry. Among these are greenhouses, which are necessary for the growth of some plants (i.e., vegetables, flowers, etc.) in severe climates. However, greenhouses require some air conditioning process to control their temperature and relative humidity to suit specific plants. To achieve this, a novel air humidifier and/or dehumidifier systems using mop fans had been designed and employed in an experimental greenhouse to evaluate its performance under a controlled environment. This device helped to reduce the energy consumption of the greenhouse whilst providing a pleasant environment for the plants inside the greenhouse. The system was designed taking into account the meteorological conditions, which affect the environment inside the greenhouse. The performance of the system was monitored over a period of time by measuring the temperature and relative humidity of the greenhouse. Results of the monitoring have shown that the system was able to provide comfortable conditions (temperatures of 16-26 oC and relative humidity of 65%) suitable for the plants grown in the experimental greenhouse. It also enabled the minimisation of temperature variation and, hence, avoided the hazard of any sudden climatic change inside the greenhouse.

CURAH HUJAN DAN SUHU UDARA TERHADAP KEJADIAN MALARIA DI KOTA BENGKULU

High and low incidence of malaria can be controlled by physical environmental conditions of the breeding of Anopheles mosquitoes. Data malaria cases in Bengkulu City for the last 8 years were 1.545, 934, 310, 144, 145, 25, 12 and 3 cases. The biggest factor affected mosquito breeding is climate. The purpose of study analyzed the effects of rainfall, temperature against the incidence of malaria in Bengkulu City. An ecological study using data from Dinkes and BMKG. The dependent variable is malaria incidence and independent variable was rainfall and temperature. Data analysis using univariate, bivariate and multivariate. The result of malaria incidence from 2012-2015 was 1,439 cases with rainfall 108-405 mm and temperature range between 25.57-26.740C. Normality test showed that the three normal distributed variables, Pearson correlation test between rainfall and malaria incidence there is a significant relationship, while the temperature and incidence of malaria there is no significant relationship. Multivariate analysis showed a weak relationship between rainfall and malaria incidence (r = - 0.173). The value of chi square coefficient (R2 = 0.3) means that the regression line equation describes 30% of the variation of malaria occurrence that is affected by rainfall. Suggestions for the results of this study can be to solve the strategy in combating malaria by anticipating if there is a sudden climate change that causes the high number of mosquito breeding and expected community participate in the prevention of malaria disease by maintaining the cleanliness of 3M environment especially in the rainy month.

CRISPR/Cas9-Mediated Mutagenesis of BrLEAFY Delays the Bolting Time in Chinese Cabbage (Brassica rapa L. ssp. pekinensis)

Chinese cabbage has unintended bolting in early spring due to sudden climate change. In this study, late-bolting Chinese cabbage lines were developed via mutagenesis of the BrLEAFY (BrLFY) gene, a transcription factor that determines floral identity, using the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) system. Double-strand break of the target region via gene editing based on nonhomologous end joining (NHEJ) was applied to acquire useful traits in plants. Based on the 'CT001' pseudomolecule, a single guide RNA (sgRNA) was designed and the gene-editing vector was constructed. Agrobacterium-mediated transformation was used to generate a Chinese cabbage line in which the sequence of the BrLFY paralogs was edited. In particular, single base inserted mutations occurred in the BrLFY paralogs of the LFY-7 and LFY-13 lines, and one copy of T-DNA was inserted into the intergenic region. The selected LFY-edited lines displayed continuous vegetative growth and late bolting compared to the control inbred line, 'CT001'. Further, some LFY-edited lines showing late bolting were advanced to the next generation. The T-DNA-free E1LFY-edited lines bolted later than the inbred line, 'CT001'. Overall, CRISPR/Cas9-mediated mutagenesis of the BrLFY gene was found to delay the bolting time. Accordingly, CRISPR/Cas9 is considered an available method for the molecular breeding of crops.

THE WORLD’S BIGGEST CALAMITY: CLIMATE CHANGE OR WARS

Climate change is the biggest global threat which has the potential to directly affect the planetary life. Its damages are beyond imagination and are not irrecoverable. The hazardous effects of climate change are exacerbating day-after-day but the worst part is that developed nations have not yet ascertained the gravity of the situation and have virtually turned deaf ears to the recommendations of international environmental protection agencies and repeated warnings of environmental scientists. Considering such an attitude, one may foresee that those days are not far ahead when the world may witness any chaotic situation or major environmental disaster. These disasters may have magnanimous impact on the future of mankind and are not akin to war which usually happens on contemporary issues and only on regional basis. War may be discussed as disastrous but it could not be so disastrous when compared with any climatic disaster. Although, the world appears to be unanimous to bring about peace and security across the globe and not to engage in any war yet sudden climatic changes may change the whole scenario and turn the planet to the position where war on any environmental issue becomes indispensable. In order to minimize the perilous effects of climate change, concerted efforts at global level are extremely necessary. The article focuses on the issue that climate change is more dangerous as compared to wars for the reason that existence and continuity of life on the planet earth is more important than any other issue for the mankind.

Driving mechanism of digital economy based on regulation algorithm for development of low-carbon industries

In recent years, natural disasters such as sudden changes in climate, earthquakes, typhoons, storms, and snowstorms have occurred frequently around the world, which have greatly threatened the survival and development of human beings. Many countries in the world are seeking a balanced development between economic development and natural environment. Among them, low-carbon economy is the priority choice, which has been accepted by all countries in the world. A low-carbon economy characterized by low energy consumption, low pollution and low emissions has become the mainstream trend in today's world. As a concept with a relatively broad connotation, the digital economy can be included in any economic form that directly or indirectly uses data to guide resources to play a role and promote the development of productivity. In this context, the driving mechanism of the digital economy for the development of low-carbon industries has also become one of the issues that every-one pays attention to. DEA (Data Envelope Analysis) is a linear programming model expressed as the ratio of output to input. It attempts to maximize the efficiency of a service unit by comparing the efficiency of a particular unit with the performance of a group of similar units providing the same service. In response to the above problems, this paper would use the DEA model to analyze the impact of the interaction between low-carbon industries and finance. It indirectly shows the relationship between the digital economy and the driving mechanism of low-carbon industry development. The experimental research shows that: under the same other conditions, there is an obvious positive relationship between environmental protection investment and financing and its output value. That is to say, the more the amount of environmental protection investment and financing in the low-carbon industry, the higher the industrial output value, which shows that the environmental protection financing has a good role in promoting the driving mechanism of the development of the low-carbon industry. It is confirmed that the digital economy can promote the driving mechanism of low-carbon industry development.

New evidence of the emergence of the East Asian monsoon in the early Palaeogene

Previous palaeoenvironmental reconstructions have implied that East Asia was dominated by a zonal climate pattern during the Eocene, with an almost latitudinal arid/semiarid band at ~ 30° N. However, this long-standing model has recently been challenged by growing body of multidisciplinary evidence. Some studies indicated that central China was characterized by climatic fluctuations between humid and drier conditions during the Early Eocene, akin to the present East Asian monsoon (EAM) regime. Using palynological assemblages in the Tantou Basin, central China, we quantitatively reconstructed climate changes from the Late Palaeocene to Early Eocene to better understand climate change in central China. Palynological assemblages revealed that the coniferous and broad-leaved mixed forest in this area received no less than 800 mm of annual precipitation and experienced a climate change from warm and wet to relatively cool and dry. According to palaeoclimate curves, a sudden climate change occurred in the Early Eocene, with the mean annual temperature and precipitation decreasing by 5.1 °C and 214.8 mm, respectively, and the climate became very similar to the present climate, which is controlled by the monsoon. Therefore, this significant climate change during the Early Eocene may signal the emergence of the EAM in East Asia.

Spatio-temporal distribution of pollutant trace gases (CO, CH4, O3 and NO2) in India: an observational study

ABSTRACT India is one of the largest contributors to anthropogenic emissions during the recent decade associated with its rapid economic growth in India. Trace gases are important components in the climate change process and due to that climate change, there will be a change in their atmospheric concentrations as the climate is sensitive to Earth’s; therefore, proper assessment of trace gases is necessary for ongoing sudden changes in climate. In this study, we used remote-sensing datasets from the Atmospheric Infrared Sounder (AIRS) and Ozone Monitoring Instrument (OMI) to analyze the spatio-temporal variations of four trace gases, like methane (CH4), ozone (O3), carbon monoxide (CO), and nitrogen dioxide (NO2) over India region during 2006–2015 and taken four seasons (i.e., winter, spring, summer, and winter) to interpret the seasonal variation. The project focuses on the temporal pattern of pollutant trace gases i.e., monthly, seasonal, and annual mean variations of trace gases, trend analysis of trace gases, and a comparison of the seasonal behavior of the trace gases by trend analysis was assessed. Higher concentrations of CO show east-to-west, CH4 show north-to-south, and O3 south-to-north gradient, indicating the variations in trace gases due to the impact of emissions and local meteorology. On the other hand, due to immense population density, huge traffic emissions, tremendous, polluted air, and overgrown industrial activities, total NO2 concentrations shoot up over Delhi, Lucknow, and Kolkata. Now as a result of seasonal variation in the long-range transport of air parcels and biomass burning activities, all trace gases shown significant seasonal variations in the spring season and substantially reduced in the summer season. However, in the winter season, O3 concentration evaluates minimum due to less amount of heat on cold days which leads to the reduction of O3 formation. Due to trace gases, all are significant to get regional climate variability. In this study by taking 2006 as a base year and investigate the behaviors of gases for 2007–2015 years to exhibit the increment and decrements in four seasons of all trace gases by taking the most populated 11 different cities of India.