Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

First description of the breeding biology of the North Philippine Hawk-Eagle (Nisaetus philippensis): a Journal of Ornithology

This study presents the first description of the breeding biology of the IUCN Endangered North Philippine Hawk-Eagle (Nisaetus philippensis). We describe a single pair's breeding phenology, nest characteristics, diet, chick development, and behavior through on-the-ground and remote observations from 1 February to 14 May 2020. Due to limited mobility during the COVID-19 pandemic, we improvised a video recording setup for remote monitoring and used machine learning to extract data from images. The nest was a low cup/fork type stick nest placed on a Malabulak tree (Bombax ceiba) in a heavily disturbed secondary forest. When it was first found, the incubation stage was underway and lasted for 1 month as the nestling emerged on 1 March 2020. Both adults provided parental care throughout the breeding period, with the male primarily providing food and the female attending to the nest, egg, and chick. They preyed on a wide range of vertebrates such as lizards, ground birds, bats, rodents, and domestic animals. With a single egg per clutch and a relatively long breeding cycle, the species has a slow reproductive output that may contribute to its current threatened status.

Monitoring of Black Pepper Growth at Different Elevation using Ground Data and NDVI Time Series

Implementation of remote sensing in agriculture helps to enhance crop growth monitoring especially during the Covid-19 pandemic. To enhance black pepper growth condition, a study was conducted at two study sites in Bintulu, Sarawak. Hence, this study aims (i) to construct a black pepper growth monitoring at different levels of elevation in Suka Farm (SF) and Taime Farm (TF);and (ii) to integrate limited ground data and NDVI time series from Landsat 8OLI for black pepper growth monitoring. Elevation maps were generated using Natural Neighbor (NN) based on the ground data analysed using ArcGIS 10.4 Software. Three elevation levels were classified into the lower, middle, and upper levels. Observational ground data and NDVI time series of Landsat 8 OLI were calculated using SAS 9.4 software. All parameters then correlating with the elevation levels using Pearson Correlation Coefficient. Optimum growth of black pepper growth in SF and TF was identified at an elevation range between 39m–50m. The NDVI time series also indicated equivalent results as the ground data. This study proposed that the elevation of an area gives a significant impact on black pepper growth. Besides, the NDVI time series of Landsat 8 OLI was feasible for monitoring black pepper growth.

Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels

In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation-EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during wintertime and lower NO2 levels during summertime, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load, obtained by both space-born observations and model simulations, show the undeniable variability of the NO2 load within the urban agglomerations. Furthermore, a clear diurnal variability is clearly identified by the ground-based measurements, as well as a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-borne observations, the ground-based measurements, and the chemical transport modelling simulations demonstrate unequivocally their ability to report on the air quality situation in urban locations.

Advances in air quality research – current and emerging challenges

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

Unexpected Impact of COVID-19 Lockdown on the Air Quality in the Metro Atlanta, USA Using Ground-based and Satellite Observations

We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14-April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%-49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% an ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon;Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account.

The Impact of Lockdown on eBC Mass Concentration over a Semi-arid Region in Southern India: Ground Observation and Model Simulations

In the present study, we focused on the impact of lockdown on black carbon (eBC) mass concentrations and their associated radiative implications from 01st March to 30th June 2020, over a semi-arid station, i.e., in the district of Anantapur in Southern India. The mean eBC mass concentration was observed before lockdown (01st–24th March 2020) and during the lockdown (25th March–30th June 2020) period and was about 1.74 ± 0.36 and 1.11 ± 0.14 µg m–3, respectively. The sharp decrease (~35%) of eBC mass concentration observed during the lockdown (LD) period as compared with before lockdown (BLD) period, was mainly due to the reduction of anthropogenic activities and meteorology. Furthermore, during the entire LD period, the net composite forcing at the top of the atmosphere (TOA) and at the surface (SUR) varied from –4.52 to –6.19 Wm–2 and –22.91 to –29.35 Wm–2, respectively, whereas the net forcing in the atmosphere (ATM) varied from 17.27 to 23.16 Wm–2. Interestingly, the amount of energy trapped in the atmosphere due to eBC is 11.19 Wm–2 before LD and 8.56 Wm–2 during LD. It is concluded that eBC contributes almost 43–50% to the composite forcing. As a result, the eBC atmospheric heating rate decreased significantly (25%) when compared to before lockdown days to lockdown days.

Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.