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Air quality networks in cities are costly, inconsistent, and only monitor a few pollutants. Space-based instruments provide global coverage spanning more than a decade to determine trends in air quality and address deficiencies in surface networks. Here we target cities in the UK (London and Birmingham) and India (Delhi and Kanpur) and use observations of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI), ammonia (NH3) from the Infrared Atmospheric Sounding Interferometer (IASI), formaldehyde (HCHO) from OMI as a proxy for non-methane volatile organic compounds (NMVOCs), and aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for PM2.5. We assess the skill of these products at reproducing monthly variability in surface concentrations of air pollutants where available. We find temporal consistency between column and surface NO2 in cities in the UK and India (R = 0.5-0.7) and NH3 at two of three UK supersites (R = 0.5-0.7), but not between AOD and surface PM2.5 (R < 0.4). MODIS AOD is consistent with AERONET at sites in the UK and India (R > = 0.8) and reproduces significant decline in surface PM2.5 in London (2.7% a-1) and Birmingham (3.7% a-1) since 2009. We derive long-term trends in the four cities for 2005-2018 from OMI and MODIS and for 2008-2018 from IASI. Concentrations of all pollutants increase in Delhi, suggesting no air quality improvements there, despite rollout of controls on industrial and transport sectors. Kanpur experiences a significant and substantial (3.1% a-1) increase in PM2.5. Concentrations of NO2, NH3 and PM2.5 decline in London and Birmingham likely due in large part to emissions controls on vehicles. Trends are significant only for NO2 and PM2.5. Reactive NMVOCs decline in Birmingham, but the trend is not significant, and there is a recent (2012-2018) steep (> 9% a-1) increase in reactive NMVOCs in London. This may reflect increased contribution of oxygenated VOCs from household products, the food and beverage industry, and domestic wood burning, with implications for formation of ozone in a VOC-limited city. [ABSTRACT FROM AUTHOR]

ABSTRACT Physical scaling ( SP) method is a statistical downscaling approach where model-based climate data are downscaled taking into consideration large-scale climate, elevation and land-cover at the location of interest. In this study, the downscaling skills of an ensemble of SP method and its variants and Statistical DownScaling Model ( SDSM) towards downscaling North American Regional Reanalysis ( NARR) temperature data are compared. Two downscaling approaches: direct and indirect, two versions: SP and surrounding pixel information and three functional forms: linear regression, quantile regression and generalized additive models are considered to prepare the method ensemble. To evaluate method performance, a leave-one-out cross-validation approach is adopted. Results indicate that SP method and its variants have comparable skill to SDSM. Further method skill is found to be only marginally influenced by the choice of method version and functional form, and considerably influenced by the choice of approach. The ensemble of models is thereafter used to downscale future air temperature projections made by a global climate model: FGOALS-s2. It is found that downscaled future projections are most significantly influenced by the choice of version, followed by the choice of approach and the choice of functional form in the decreasing order of importance. [ABSTRACT FROM AUTHOR]

ABSTRACT The objective of this study is to investigate the vulnerability of different land-cover types to climate change. To this end, land-cover specific temperature change factors are quantified for the southern Saskatchewan region using a novel statistical downscaling model: physical scaling ( SP). SP model considers large-scale climate and regional physical characteristics like land-cover, elevation in its formulation and hence can be used to predict future temperature for different land-cover types under changing large-scale climatic and land-cover conditions. The model is validated by assessing its ability to downscale North American Regional Reanalysis ( NARR) derived surface (skin) temperature from an initial resolution of 32 km to 500 m. The downscaled NARR data are evaluated using a cross-validation approach over the period 2006-2013 with reference to MODerate-resolution Imaging Spectroradiometer ( MODIS) derived surface temperature estimates and satisfactory model performance is obtained (average RMSE = 0.03 K). The validated model is used to predict future surface temperature across the study region. Future land-cover projections are derived by downscaling land-use projections for Representative Concentration Pathways ( RCPs) 2.6 and 8.5 made by integrated assessment models: IMAGE and MESSAGE, respectively. An analysis of land-cover specific temperature changes between historical (2006-2013) and future (2081-2100) timelines indicate variations of up to 2 K between different land-cover classes. Vulnerability pattern of different land-cover classes differ significantly between day- and night-time. Further, variations of upto 1 K in projected changes are observed among different forest cover types. Closed shrubland is obtained as the most vulnerable forest-cover class whereas evergreen broadleaf forest is found to be the least vulnerable. [ABSTRACT FROM AUTHOR]

In today's world due to population increase and fast pace of life the number of vehicles on roads have increased which leads to violation of traffic rules. This also leads to occurrence of frequent accidents. This paper proposes a technique used to control traffic rule violation. The technique is related to object detection and recognition, by which we can control traffic rule violation. We will be using poles in certain intervals on the roadside, object detection cameras and centralized database for matching the objects - face of the driver and number plate of the vehicle. The poles which will be present on the roadside will have cameras and speedometer installed on it. When a person driving, break's the traffic rule regarding speed then the camera will take picture of the driver and the number plate of the vehicle. These pictures will be matched with a centralized database which will be on cloud. The image of face and number plate should correspond to the vehicle owner. If there is a mismatch than the number plate image is given more priority. The details of the vehicle owner will be tracked from the database and compensation will be charged instantly from his or her bank account. The bank account deduction message comes instantly to the driver which psychologically stops him from committing the next violation. [ABSTRACT FROM AUTHOR]