EISSN : 2073-4433
Current Publisher: MDPI AG (10.3390)
Total articles ≅ 4,169
Latest articles in this journal
Atmosphere, Volume 12; doi:10.3390/atmos12060793
The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.
Atmosphere, Volume 12; doi:10.3390/atmos12060788
In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.
Atmosphere, Volume 12; doi:10.3390/atmos12060789
Environmental factors such as air pollution are known to exacerbate respiratory illness and increase the overall health risk. However, on a daily or seasonal basis, the relation between air pollutants, weather and a disease such as asthma is not clear. When combined with aeroallergens such as birch pollen and under specific weather conditions, synergistic effects may increase symptoms of respiratory illness and morbidity and then reveal interesting links with environmental factors. Hence, it is important to improve the understanding of pollution-pollen-weather and broaden the public health message. Combined analysis and model simulation of aeroallergens, air pollution and weather as presented here is important to correctly evaluate health burdens and allow a better forecast of the potential health risk. However, analyzing the combined effects of several environmental factors is not well understood and represents a challenging task. This paper shows: (1) the results of data analysis performed in Montreal for asthma hospitalization in relation to complex synergistic environmental factors, and (2) model simulation of birch pollen using a coupled weather-air quality model (GEM-MACH) compared with model-data fusion of classical chemical species (e.g., near-surface ozone, nitrogen dioxide and fine particulate matter) in order to evaluate spatiotemporal vulnerable zone for asthma health risk.
Atmosphere, Volume 12; doi:10.3390/atmos12060791
Recently, there has been a great increase in the importance of issues related to energy efficiency
Atmosphere, Volume 12; doi:10.3390/atmos12060792
The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change.
Atmosphere, Volume 12; doi:10.3390/atmos12060790
Association between short-term exposure to ambient air pollution and respiratory health is well documented. At the same time, it is widely known that extreme weather events intrinsically exacerbate air pollution impact. Particularly, hot weather and extreme temperatures during heat waves (HW) significantly affect human health, increasing risks of respiratory mortality and morbidity. Concurrently, a synergistic effect of air pollution and high temperatures can be combined with weather–air pollution interaction during wildfires. The purpose of the current review is to summarize literature on interplay of hot weather, air pollution, and respiratory health consequences worldwide, with the ultimate goal of identifying the most dangerous pollution agents and vulnerable population groups. A literature search was conducted using electronic databases Web of Science, Pubmed, Science Direct, and Scopus, focusing only on peer-reviewed journal articles published in English from 2000 to 2021. The main findings demonstrate that the increased level of PM10 and O3 results in significantly higher rates of respiratory and cardiopulmonary mortality. Increments in PM2.5 and PM10, O3, CO, and NO2 concentrations during high temperature episodes are dramatically associated with higher admissions to hospital in patients with chronic obstructive pulmonary disease, daily hospital emergency transports for asthma, acute and chronic bronchitis, and premature mortality caused by respiratory disease. Excessive respiratory health risk is more pronounced in elderly cohorts and small children. Both heat waves and outdoor air pollution are synergistically linked and are expected to be more serious in the future due to greater climate instability, being a crucial threat to global public health that requires the responsible involvement of researchers at all levels. Sustainable urban planning and smart city design could significantly reduce both urban heat islands effect and air pollution.
Atmosphere, Volume 12; doi:10.3390/atmos12060782
The extreme climates that occur around the world every year have a profound impact on the quality of life for mankind since they can cause natural disasters beyond our control, such as droughts and floods
Atmosphere, Volume 12; doi:10.3390/atmos12060783
The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.
Atmosphere, Volume 12; doi:10.3390/atmos12060784
Black carbon (BC) is of concern due to its contribution to poor air quality and its adverse effects human health. We carried out the first real-time monitoring of BC in Malaysia using an AE33 Aethalometer. Measurements were conducted between 1 January and 31 May 2020 in a university area in a suburban location of the Klang Valley. The measurement period coincided with the implementation of a movement control order (MCO) in response to COVID-19. The mean concentration of BC before the MCO was 2.34 µg/m3 which decreased by 38% to 1.45 µg/m3 during the MCO. The BC is dominated by fossil-fuel sources (mean proportion BCff = 79%). During the MCO, the BCff concentration decreased by more than the BCbb concentration derived from biomass burning. BC and BCff show very strong diurnal cycles, which also show some weekday–weekend differences, with maxima during the night and just before noon, and minima in the afternoon. These patterns indicate strong influences on concentrations from both traffic emissions and boundary layer depth. BC was strongly correlated with NO2 (R = 0.71), another marker of traffic emission, but less strongly with PM2.5 (R = 0.52). The BC absorption Ångström exponent (AAE) ranged between 1.1 and 1.6. We observed pronounced diurnal cycles of lower AAE in daytime, corresponding to BCff contributions from traffic. Average AAE also showed a pronounced increase during the MCO. Our data provides a new reference for BC in suburban Malaysia for the public and policy-makers, and a baseline for future measurements.
Atmosphere, Volume 12; doi:10.3390/atmos12060785
Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM10, PM2.5), NO2, CO, and SO2 concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO2 concentration was higher, that of PM2.5 was higher in neighborhoods with more bus stops, and those of CO and PM2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.