Validation of PM10 and PM2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July-August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

Updating and evaluating the NH3 gas-phase chemical mechanism of MOZART-4 in the WRF-Chem model

The accuracy of determining atmospheric chemical mechanisms is a key factor in air pollution prediction, pollution-cause analysis and the development of control schemes based on air quality model simulations. However, the reaction of NH3 and OH to generate NH2 and its subsequent reactions are often ignored in the MOZART-4 chemical mechanism. To solve this problem, the gas-phase chemical mechanism of NH3 was updated in this study. Response surface methodology (RSM), integrated gas-phase reaction rate (IRR) diagnosis and process analysis (PA) were used to quantify the influence of the updated NH3 chemical mechanism on the O3 simulated concentration, the nonlinear response relationship of O3 and its precursors, the chemical reaction rate of O3 generation and the meteorological transport process. The results show that the updated NH3 chemical mechanism can reduce the error between the simulated and observed O3 concentrations and better simulate the O3 concentration. Compared with the Base scenario (original chemical mechanism simulated), the first-order term of NH3 in the Updated scenario (updated NH3 chemical mechanism simulated) in RSM passed the significance test (p < 0.05), indicating that NH3 emissions have an influence on the O3 simulation, and the effects of the updated NH3 chemical mechanism on NOx-VOC-O3 in different cities are different. In addition, the analysis of chemical reaction rate changes showed that NH3 can affect the generation of O3 by affecting the NOx concentration and NOx circulation with radicals of OH and HO2 in the Updated scenario, and the change of pollutant concentration in the atmosphere leads to the change of meteorological transmission, eventually leading to the reduction of O3 concentration in Beijing. In conclusion, this study highlights the importance of atmospheric chemistry for air quality models to model atmospheric pollutants and should attract more research focus.

Design of an energy vulnerability index - spatial and temporal analysis: case of study Colombia

Climate change might affect energy production and therefore the energy security of a country or region. This situation may impact renewable energy sources such as hydro power, leading to consequences on energy transition strategies. This might be critical in sensitive regions to climate change, one of them being the Caribe and northern South America. Since there are numerous energy systems based on sensitive technologies worldwide, it is necessary to introduce techniques to analyze the effects of climate change on different possible energy transition paths. The goal of this study is to develop and assess a method to analyze one of the most critical effects faced by climate change for societies worldwide: the sensitivity of the energy systems to climate change. This is especially critical in developing countries, in locations where temperatures will strongly increase in the following years. To assess this effect, this study proposes a vulnerability index (VI) to evaluate the vulnerability of an on-grid electricity system to climate change at the national and regional scales. This index was assessed using a Monte-Carlo method for uncertainty. The case of Colombia, a country with a system based on hydropower (> 70%) is used to illustrate the method. VI is based on variables related to climate change, the energy matrix, and vulnerability. Results show that the regions with the larger vulnerability correspond to the more energy-demanding ones. The VI for these regions is greater than 50% of the maximum possible vulnerability; meanwhile, the vulnerability of the whole country was estimated as 43%.

Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.

Classification Prediction of PM10 Concentration Using a Tree-Based Machine Learning Approach

The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution.

Impact of lockdown on particulate matter concentrations in Colombia during the COVID-19 pandemic

The first confirmed case of COVID-19 in Colombia was reported on March 6, 2020. For this reason, on March 25, preventive isolation was declared mandatory. These measures involved the suspension of economic activities and drastically reduced the number of vehicles on the road. The objective of this study is to evaluate the impact of the lockdown due to the COVID-19 pandemic on PM_2.5 concentrations at 5 monitoring stations and aerosol optical depth values of the Terra/MODIS satellite. We analyzed and compared the weekly and monthly concentrations of PM_2.5 before and during the lockdown between the week of January 6 to June 22, 2020, and compared the daily values obtained from the Terra/MODIS satellite for the months of January-June during the years 2018-2020 to elucidate the effects of the lockdown. Similar to other monitored sites in the world, we observed substantial reductions in weekly PM_2.5 concentrations, from 41 to 84% (Bogotá), from 13 to 66% (Funza), from 17 to 57% (Boyacá), from 35 to 86% (Valledupar) and 31 at 60% (Risaralda). Unlike other studies, the aerosol optical depth values increased up to 59% during the months of lockdown compared to previous years and up to 70% of the weekly mean when compared to before the lockdown. These spatiotemporal behaviors of PM_2.5 and the aerosol optical depth in Colombia are influenced by reductions in vehicular flow during quarantine, regional rainfall, and height of the planetary boundary layer. Emissions from economic activities affect pollutant levels in the area. The analysis of the levels of pollutants during the lockdown provides a baseline for regulatory agencies to establish mitigation plans.

Implementation and evaluation of WRF simulation over a city with complex terrain using Alos-Palsar 0.4 s topography

Air quality modeling requires an accurate representation of meteorology, and in cities with complex topography, the performance of meteorological modeling can be improved by using an alternative global digital elevation model (GDEM) such as Alos-Palsar 0.4 s instead of the default elevation data. Bogotá is a city with complex topography geographically located over the Andes Mountains at 2600 m.a.s.l. A reliable meteorological simulation model is critical for performing a suitable air quality modeling in any case of study. Previous researches have been developed using the standard Weather Research and Forecast (WRF) topography (GTOPO 30 s). These studies have been developed with different configurations for the representation of meteorology. The aim of this study is to evaluate Alos-Palsar 0.4 s topography with WRF, and two domain configurations with horizontal spatial resolutions up to 1000 m, to establish a reliable and accurate way to simulate the meteorology in the city of Bogotá. The evaluation quantitative parameters: IOA, r (Pearson), RMSE, MGE, and MB were calculated for the quantitative evaluation of temperature, relative humidity, wind speed, wind direction, and solar radiation. An additional evaluation using Taylor diagrams was performed. Spatial differences were identified in the same locations as well the differences between the elevation from Alos-Palsar 0.4 s and GTOPO30. The results and evaluation suggest that simulations based on Alos-Palsar 0.4 s topography lead to a significant improvement in the meteorology representation by WRF in a region with complex topography such as Bogotá, Colombia.