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Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model's training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world. [ABSTRACT FROM AUTHOR]

Thirty four avian Escherichia coli isolates were collected from different cities of Punjab province, Pakistan during 2008-2009. Twenty five phenotypic highly ampicillin-resistant (MICs ≥ 256 μg/ml) avian E. coli strains were selected for the investigation of occurrence and transmission of class 1, 2 and 3 integrons and β-lactamase genes. Amoxicillin, sulfonamide, trimethoprim, enrofloxacin, pefloxacin and tetracycline were the most common phenotypic resistant among ampicillin-resistant avian E. coli strains. Integrons and β-lactamase were found 60 and 72% respectively. Class 1 and 2 integrons were found 52 and 8%, while class 3 integrons were not found in all strains. All class 1 positive strains had variable fragments associated with gene cassettes dfrA7, dfrA1- aadA1, aadA1, aadA22 and dfrA12- orfF- aadA2 respectively, which confer resistance to trimethoprim and streptomycin. Class 2-positive strains had similar gene cassettes array dfrA1- sat1- aadA1 conferring resistance to trimethoprim, streptothricin and spectinomicin/streptomycin. Integrons are frequently found in β-lactamase positive isolates and widely disseminate multidrug resistance genes but they do not play role in the spreading of β-lactamase genes. Class 1 integrons gene cassette aadA22 is reported for the first time in avian E. coli. Findings of this study may provide important and useful information reflecting specific antibiotic selective pressure in Punjab province, Pakistan. [ABSTRACT FROM AUTHOR]