The RNN technique used to calculate pollution load has a direct influence on quantifying pollution's impact on air quality and on the overall assessment results. As a result, the weight of each assessment criteria must be determined in a convoluted and thorough manner. This Fuzzy is engaged in...
environmental justice. As India grapples with the immediate consequences of air pollution, emerging challenges require attention. Also, climate change exacerbates existing issues, influencing weather patterns and contributing to the persistence of stagnant air masses that trap pollutants and their transportati...
Python Air Quality View more details Feb 24th 2025 Course Auditing Coursera DeepLearning.AI Data Science Beginner 3 Weeks 1-4 Hours/Week 45.00 EUR/month English English Air Pollution – a Global Threat to our Health (Coursera) We all have to breathe to live. But the air...
Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data...
The Python package TensorFlow, a deep learning framework, was used to train CNNs using the Keras library. Due to the large size of the ImageNet dataset, that covers approximately 1.2 million images, it is often used to build numerous architectures for generating general models. To achieve ...
Detection and classification of trash using computer vision has been studied in a multitude of previous research applications. Thung and Yang presented a fine-tuned CNN for the classification of garbage with respect to their Trashnet dataset [12]. The classification accuracy of their prediction model...
Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of
The CIDW could map air pollution much more effectively in the months and days of higher RMSE than was achieved in the studies that used ordinary kriging, the IDW, and land use regression approaches, because of the features in spatial clustering based on densely-deployed microsensors. Table 1....
From Fig.4b,d, it can be seen that, after the lockdown policy was implemented, AQI dropped dramatically compared to the historical period. This is because traffic and factory pollution decreased during the lockdown. To this end, we need to eliminate the influence of these external factors. ...
Air pollution exposures during training may impact race performances. We aggregated data on 334 collegiate male track & field athletes from 46 universities across the United States over 2010–2014. Using distributed lag non-linear models, we analyzed