Wildfires emit large quantities of air pollutants into the atmosphere. As wildfires increase in frequency, intensity, duration, and coverage area, the emissions from these fires have become a significant control issue and health hazard for residential populations, especially the vulnerable groups. A critical barrier to addressing the health impacts of air pollution caused by wildfires lies in our limited understanding of its true extent. This problem is expected to be exacerbated by additional factors such as the anticipated increase in wildfire intensity due to climate change, and the associated rise in fine particulate matter (PM_2.5) in wildfire smoke, which, according to recent toxicological studies, could be more harmful than typical ambient PM_2.5. The primary goal of our study is to develop a novel statistical framework that enables the forecasting of future emissions from active wildfires. This research aims to address the unquantified impacts of wildfire emissions and is a priority research area for many US federal agencies e.g., NIEHS, US EPA, and NOAA. The framework integrates physiochemical models of emissions and satellite observations with forecasting models based on spatial statistics and deep learning. Through the incorporation of these diverse datasets, we aim to improve the accuracy and reliability of our predictions regarding the spatio-temporal distribution of wildfire emissions. The potential human health impacts resulting from poor air quality during wildfires are also explored. By modeling the relationship between environmental exposures and disease risk, the burden of disease attributed to both short- and long-term impacts of exposure to wildfire events will be assessed.

The air quality of Himalayan region of India is deteriorating due to the increasing load of particulate matter that is emitted from various local and regional sources as well as transit of dust-related pollutants from the Indo-Gangetic Plain (IGP) and surrounding areas. In this study, the mineralogical characteristics of coarse mode particulate matter (PM₁₀) has been analyzed using X-Ray Diffraction (XRD) technique from January–December, 2019 over Nainital (29.39°N, 79.45°E; altitude: 1958 m above mean sea level), a central Himalayan region. XRD analysis of PM₁₀ samples showed the presence of clay minerals, crystalline silicate minerals, carbonate minerals, and asbestiform minerals. It is shown that the quartz minerals with significant levels of crystallinity were present in all the samples. Other minerals that are contributing to the soil dust are also observed in the analysis (CaFe₂O₄, CaCO₃, CaMg(CO₃)₂, calcium ammonium silicate (CAS), gypsum, feldspar, kaolinite, illite, augite, montmorillonite, and vaterite). The minerals, ammonium sulphate, hematite, koktaite, and magnetite were also found in the samples and are suggested to be from the biogenic and anthropogenic activities including biomass burning, fuel combustion, vehicle exhaust, etc. This study indicated that the majority of the minerals in PM₁₀ that were present in this Himalayan region are from soil/crustal dust.

A comprehensive study was carried out in the urban area of Kenitra City, Morocco, covering the period from 2020 to 2021. During this study, 60 effective PM_2.5 samples were collected for a duration of 24 hours using a dichotomous sampler and Nuclepore track-etched polycarbonate filters with a diameter of 37 mm. Ion chromatography was employed to determine the content of water-soluble ions, including SO₄²⁻, NO₃⁻, Cl⁻, F⁻, NH₄⁺, Ca²⁺, Na⁺, and K⁺.

The results revealed that the average mass concentration (± standard deviation) of the six ions in PM2.5 was 3.24±1.32 µg.m^-3, constituting approximately 18.7% of the total mass concentration. Among the ions, the concentrations followed the order of Na⁺ > NO₃⁻ > SO₄²⁻ > K⁺ > F⁻ > NH₄⁺ > Cl⁻. The mass concentration and most of the chemical components of PM_2.5 exhibited significant variations throughout the seasons, with higher levels observed during the summer and lower levels during the winter.

The major components of water-soluble ions in PM_2.5 were found to be secondary inorganic species (NO₃^-, SO₄^2- and NH₄⁺) and sea salt species (Na⁺ and Cl^-), contributing an average of 78% to the total PM_2.5 ions. The concentrations of these three ions varied across the four seasons, with the highest levels observed in summer, followed by winter, autumn, and spring. Notably, NH₄⁺ showed a significant correlation with SO₄²⁻, with a maximum correlation coefficient of 0.85 during summer and a minimum coefficient of 0.39 during autumn. The ratio of [NO₃⁻]/[SO₄²⁻] was found to be lower than unity, indicating that the main sources of sulfur and nitrogen in the Kenitra atmosphere were prioritized from stationary sources (typically
associated with power plants, industrial and commercial activities, and other large-scale facilities).

Wildfires threaten human lives and ecosystems and have a significant impact on the economy. Greece is one of the most vulnerable countries in the world with respect to wildfires. The purpose of this article is to assess the climate change impact of wildfires on the ecosystems of Greece and to determine areas where prevention measures should be utilized. To achieve this, the variability of the Fire Weather Index (FWI) is examined under the RCP4.5 and RCP8.5 scenarios from 2022 to 2098. The Greek ecosystems are selected based on the vegetation zones that cover the Greek domain. Under both scenarios, a significant intensification of fire weather is observed which will increase the likelihood of severe wildfires occurring in various types of ecosystems in Greece. The worst affected areas are southern and eastern Greece provided that they have sufficient fuel. The results are more pronounced for the RCP8.5, especially after mid-century. By the end of the century, most ecosystems will be prone to intense fire activity under RCP8.5, and for example, the “Mediterranean coniferous forests” in high altitudes could face extinction. Even under the milder RCP4.5 scenario, high-intensity wildfires are projected to occur with increasing frequency in places that are currently rare. This project highlights the necessity of climate change mitigation and the employment of more effective and widespread prevention and firefighting methods. The management of currently fire-prone areas should be emphasized but the state must be prepared to face extreme fire incidents in a broader range of ecosystems including mid-altitude and even high-altitude forests.

This study presents a comprehensive sensitivity analysis of the deflection of the cyclone track over isolated topography, focusing on exploring the impact of the direction and strength. A dynamic model is used to investigate the track adjustment of strong cyclonic vortices on a β-plane in an isolated topographic feature. Considering the conservation of potential vorticity, we derived a meridional adjustment velocity (MAV), which provides a first-order adjustment for the vortex motion. Detailed sensitivity calculations examine the variations in track patterns under different flow conditions, including impinging direction, vortex strength, and maximum topographic height. The analysis reveals an S-shaped pattern in most tracks, characterized by a southward deflection on the windward side and a northward recovery on the lee side of the vortex motion. In particular, when the vortex passes over the high-rise feature in the middle of the terrain, it experiences a significant deflection in its path, which poses challenges to accurate prediction of the tropical cyclone path. Furthermore, the study demonstrates that larger direction angles of the vortex that reaches the terrain result in more pronounced path deflections. The observed track deflection is attributed to the terrain loop effect induced by the strong topographic β effect. Furthermore, from a path prediction sensitivity perspective, adjacent vortex paths on the windward side converge on the leeward side in the south of the terrain, improving track prediction accuracy. On the contrary, vortices crossing the mountain from the north tend to diverge on the leeward side, reducing the accuracy of path prediction. In conclusion, considering various flow conditions, this dynamic model offers valuable insights into the uncertainties associated with path prediction for strong cyclones over topography. It also highlights the potential to incorporate higher-order adjusting velocity components and advanced vortex models to improve the accuracy of path prediction.

The layer of Earth's atmosphere known as the ionosphere presents a significant obstacle to global satellite navigation systems (GNSS) due to its ability to introduce errors. To address this challenge, various navigation systems have introduced new signals designed to minimize errors caused by the ionosphere. These signals not only aid in error reduction but also facilitate the examination of electron content behavior. This research focuses on the analysis of ionospheric maps obtained from RINEX data collected at the INEG station in Aguascalientes, Mexico, from 2011 to 2018, with a particular emphasis on highly intense geomagnetic storms characterized by values below -100 nT. The analysis of these maps employs the Probability Density Function (PDF), which allows for the representation of data distribution on graphs. This distribution is then examined in conjunction with the station's Total Electron Content (TEC) values and the Dst index during the corresponding geomagnetic storm events. The findings establish the correlation between each of these parameters during such events.

Household air pollution was responsible for an estimated 3.2 million deaths per year in 2020, including over 237, 000 deaths of children under the age of 5. Large number these death case was particularly recorded in developing countries where many people rely heavily on biomass for energy. Burning biomass emits carbon monoxide and other pollutants resulting to indoor air pollution, exacerbations of asthma, hospitalizations for heart attacks and respiratory illness, birth defects, neurological diseases, and even mortality are all brought on by indoor air pollution. Because women and children typically do most of the cooking, they are most affected by indoor air pollution.. In this research active sampling technique was adopted in estimating the amount of three major criteria gaseous pollutants (CO, H₂S and SO₂) in the air in rural household kitchen within Jos metropolis. The Attair 5X gas detector was used. the power button was pressed and the equipment was allowed to initialize for few minutes, while, the readings was taken downwind in-situ at a distance of 1m, 2m, 3m, 4m, and 5m respectively from the emission source at the expiration of one (1) minute for each distance to check the impact of emission on the environment and people in such area. Result obtained shows CO, H₂S and SO₂ where higher from firewood emission source when compared with charcoal emission sources from the 14 different rural kitchen in Bauchi ring road, Jos, Plateau State, Nigeria. Hence this study serve as a ready reference for environmentalists to make target decision on air pollution reduction.

Air pollution is a serious issue in most parts of Bihar, especially in its capital city, Patna. The air quality in Patna has significantly worsened due to factors like rapid urbanization, increased traffic, and various natural and human-related causes. This decline in air quality has led to several negative health effects. In light of this, the aim of this study was to examine how air pollution affects the short-term and long-term health of Patna's residents, taking into account age and exposure time as important factors. To conduct the study, we collected air quality data from the previous eight (2013-2020), which we obtained from the Bihar State Pollution Control Board's website. By using extrapolation techniques, we were able to make predictions about the city's future air quality. We gathered data from two busy intersections in Patna, specifically Danapur and Digha. Health effects from air pollution was collected from the residents by a formatted questionnaire. To analyse the relationship between age, exposure time, and the health effects reported by the participants, we used a statistical test called the chi-square test of independence. The findings of the study revealed a clear link between age, exposure time, and the health status of the participants. We concluded that older individuals and those with longer exposure times faced a higher risk associated with the increasing air pollution levels. This study provides a foundation for raising awareness among both authorities and the general public of the adverse health impacts associated with declining air quality, emphasizing the urgency in taking appropriate measures to counter this challenge.

The optical and radiative properties of aerosols are governed by their types. In this paper, the optical and radiative properties of different aerosol types in Wuhan, China, have been inverted and investigated by the collected PM_2.5 samples. The results show that PM_2.5 (average mass concentration about 31.25 μg/m³) is mainly contributed by sulfate (SO4) and organic carbon (OC) (22% and 52%, respectively), while aerosol optical depth (AOD, average about 0.28) is mainly contributed by SO4 and black carbon (EC) (22% and 19%, respectively). SO4 and nitrate (NO3) have a negative radiative forcing at the top of the atmosphere (TOA) and a cooling effect, while OC and EC have a positive radiative forcing and a heating effect. Moreover, EC has the most significant effect on the radiative forcing in Wuhan, contributing up to 61% and 73% at the bottom of the atmosphere (BOA) and atmosphere (ATM), respectively, while contributing up to 75% to the atmospheric heating rate (about 1~2 K/day).

The objective of the study was to statistically analyze the concentration of pollution in the air by PM_2.5 particulate matter and vehicular traffic using a methodology based on quadrants, considering the periods of "working" and "holiday" activities in the downtown area from the port city of Tampico, Mexico. The vehicular traffic count was carried out in three hourly ranges per day for a week in each period. Moreover, an analysis of the correlation coefficient based on the Spearman method was carried out, both in the working and vacation periods, between the variables of PM_2.5 concentration and total vehicular traffic by the hourly range and by day in each quadrant. A strong to very strong correlation (0.828 ≥ r_s ≤ 0.960) was identified between pollutant concentration and vehicular traffic on several days and quadrants in the work period. On the other hand, in the holiday period, a weak to moderate correlation was observed on most of the days considered in the study.