Our understanding of the processes that control the burden and budget of tropospheric ozone has changed dramatically over the last 60 years. Models are the key tools used to understand these changes, and these underscore that there are many processes important in controlling the tropospheric ozone budget. In this critical review, we assess our evolving understanding of these processes, both physical and chemical. We review model simulations from the International Global Atmospheric Chemistry Atmospheric Chemistry and Climate Model Intercomparison Project and Chemistry Climate Modelling Initiative to assess the changes in the tropospheric ozone burden and its budget from 1850 to 2010. Analysis of these data indicates that there has been significant growth in the ozone burden from 1850 to 2000 (approximately 43 ± 9%) but smaller growth between 1960 and 2000 (approximately 16 ± 10%) and that the models simulate burdens of ozone well within recent satellite estimates. The Chemistry Climate Modelling Initiative model ozone budgets indicate that the net chemical production of ozone in the troposphere plateaued in the 1990s and has not changed since then inspite of increases in the burden. There has been a shift in net ozone production in the troposphere being greatest in the northern mid and high latitudes to the northern tropics, driven by the regional evolution of precursor emissions. An analysis of the evolution of tropospheric ozone through the 21st century, as simulated by Climate Model Intercomparison Project Phase 5 models, reveals a large source of uncertainty associated with models themselves (i.e., in the way that they simulate the chemical and physical processes that control tropospheric ozone). This structural uncertainty is greatest in the near term (two to three decades), but emissions scenarios dominate uncertainty in the longer term (2050–2100) evolution of tropospheric ozone. This intrinsic model uncertainty prevents robust predictions of near-term changes in the tropospheric ozone burden, and we review how progress can be made to reduce this limitation.

Particulate matter (PM) plays a vital role in altering air quality, human health, and climate change. There are sparse data relevant to PM characteristics in urban environments of the Middle East, including Peshawar city in Pakistan. This work reports on the morphology and composition of PM in two size fractions (PM2.5 and PM10) during November 2016 in Peshawar. The 24 hous mass concentration of PM2.5 varied from 72 µg m–3 to 500 µg m–3 with an average value of 286 µg m–3. The 24 hours PM10 concentration varied from 300 µg m–3 to 1440 µg m–3 with an average of 638 µg m–3. The morphology, size, and elemental composition of PM were measured using Fourier Transform Infra Red (FT-IR) Spectroscopy and Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray (EDX) Spectroscopy. The size of the analyzed particles by EDX ranged from 916 nm to 22 µm. Particles were classified into the following groups based on their elemental composition and morphology: silica (12%), aluminosilicates (23%), calcium rich (3%), chloride (2%), Fe/Ti oxides (3%), carbonaceous (49%), sulfate (5%), biogenic (3%). The major identified sources of PM are vehicular emissions, biomass burning, soil and re-suspended road dust, biological emissions, and construction activities in and around the vicinity of the sampling site.

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. $71{\mathrm{km}}^2$ in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

Pesticides are one of the main organic pollutants as they are highly toxic and extensively used worldwide. The reclamation of wastewater containing pesticides is of utmost importance. For this purpose, GO-doped metal ferrites (GO-Fe₃O₄ and GO-CoFe₂O₄) were prepared and characterized using scanning electron microscopy, X-ray diffraction and Fourier transform infrared spectroscopic techniques. Photocatalytic potentials of catalysts were investigated against acetamiprid’s degradation. A detailed review of the parametric study revealed that efficiency of overall Fenton’s process relies on the combined effects of contributing factors, i.e., pH, initial oxidant concentration, catalyst dose, contact time, and acetamiprid load. ~97 and ~90% degradation of the acetamiprid was achieved by GO-CoFe₂O₄ and GO-Fe₃O₄, respectively during the first hour under UV radiations at optimized reaction conditions. At optimized conditions (i.e., pH:3, [H₂O₂]: 14.5 mM (for Fe₃O₄, GO-Fe₃O_4, and GO-CoFe₂O₄) and 21.75 mM (for CoFe₂O₄), catalysts: 100 mgL⁻¹, time: 60min) the catalysts exhibited excellent performance, with high degradation rate, magnetic power, easy recovery at the end, and efficient reusability (up to 5 cycles without any considerable loss in catalytic activity). A high magnetic character offers its easy separation from aqueous systems using an external magnet. Moreover, the combined effects of experimental variables were assessed simultaneously and justified using response surface methodology (RSM).