At present, China is facing substantial pressure to reduce carbon emissions (CE). In the context of China's dual carbon goals, the accounting and peak prediction of CE in provinces along the Yellow River Basin (YRB) are crucial to China's carbon reduction strategy. In this study, the IPCC inventory method was used to calculate the CE of the provinces in the YRB, and the spatial and temporal evolution characteristics of CE are described. The Tapio decoupling model was used to explore the decoupling relationship between CE and economic growth. By establishing the stochastic impacts by regression on population, affluence and technology (STIRPAT)-ridge model, based on the impacts of CE drivers, the carbon peak of each province is predicted, and a differentiated carbon peak path is proposed. The results indicated that from 2005 to 2020, the total CE amount in the YRB continued to rise, but the growth rate continued to decline. Provinces experienced many changes involving strong decoupling, and the decoupling state varied. Factors such as the permanent resident population, economic level, technological level, urbanization level, industrial structure, and energy intensity drove CE changes, of which population was the main CE driving force. CE in the YRB will peak between 2030 and 2044, and the cumulative emissions will be in the range of 6913.96–16248.6 Mt CO2eq, while 2030 is suggested as the best year to peak. The research results provide a scientific basis for different regions to formulate differentiated carbon emission reduction plans.

Atmospheric black carbon (BC) particles resulting from the incomplete combustion of both fossil and non-fossil sources recently gained significant attention globally due to their potential health impacts. This study aimed to investigate the temporal trends of ambient BC in urban traffic (Sharif station) and background (Setad-e-Bohran station) air pollutants monitoring sites in Tehran, and to estimate its health and economic burdens from March 2017 to March 2018. The mean BC concentration in the traffic and urban background sites in cold seasons was 6.4 μg/m3 and 3.4 μg/m3. During the warm season, these figures were 4.4 μg/m3 and 2.3 μg/m3, respectively. Our observations indicated that ambient BC concentration was lower during weekends, more likely due to decreased traffic levels compared to weekdays. Our results showed that the concentration of BC and the BC/PM2.5 ratio were higher during nighttime in Tehran, likely due to high atmospheric stability and increased transit of heavy-duty diesel vehicles. We found strong correlation coefficients between BC, CO (BC–CO, r = 0.83, p < 0.01), NO2 (BC– NO2, r = 0.64, p < 0.01), PM2.5 (BC- PM2.5, r = 0.89, p < 0.01) and other components of PM2.5 (BC- other components of PM2.5, r = 0.81, p < 0.01). We estimated that long-term exposure to ambient air BC resulted to 424 (95% Confidence Interval (CI): 400–510) deaths in adults ≥25 y/r from all-natural causes. Mortality due to BC exposure for ischemic heart disease, stroke, chronic obstructive pulmonary disease and lung cancer were 82, 28, 25, and 16, respectively. Exposure to BC caused an economic loss of 161.6 [95% CI: 105.5–213.8] million US$ due to all-cause mortality. Almost 11% of mortality and economic loss of PM2.5 in Tehran is due to BC, which can be avoided by adopting and implementing appropriate and sustainable air quality abatements.

Recent years have seen an increase in regional ozone (O3) pollution in China. This study explored the interannual variability in daily maximum 8-h average O3 concentration (MDA8-O3) in Northeast China (NEC) and its three subregions (Liaoning, Jilin, and Heilongjiang) during 2013–2021, and identified the key meteorological drivers underlying the observed variability. The Kolmogorov–Zurbenko filtering technique was applied to a stepwise multiple linear regression model to decompose the meteorological and anthropogenic contributions to annual MDA8-O3 trends. The results showed that the spatiotemporal variation of MDA8-O3 in NEC is characterized by a high–south and low–north pattern with an MDA8-O3 hotspot in the Bohai Rim area. Over the 9-year study period, a significant increasing trend (∼2.5% a−1, P < 0.05) in the regional mean of the annual 90th percentile of MDA8-O3 was detected across NEC. This trend was strongly relevant to changes in relative humidity and surface solar radiation downward (SSRD). Statistical analyses revealed that SSRD dominated MDA8-O3 variability spatially over almost the entire NEC region. Additionally, the contribution decomposition suggested that the trend of increase in annual average MDA8-O3 in NEC was dominated by anthropogenic emission change, which explained 91.9% of the total variation. Regionally, although the dominant role of emissions has not changed, the meteorology-driven anomalies also explained −4.3%–3.3% of the annual average MDA8-O3 variation. This study provides insight into decoupling the complex relationships between long-term variability in regional O3 pollution and both anthropogenic emissions and meteorology, which could provide valuable information for future efforts to address the O3 pollution in NEC.

In this study, air quality monitoring nodes (equipped with pollutants (NO, NO2, CO, and PM2.5), temperature, and humidity sensors) were deployed at seven locations inside a road tunnel for continuous multipoint monitoring of traffic emissions. At the same time, in-tunnel turbulence was measured with a 3D-sonic anemometer in the middle of the tunnel to estimate vehicle-induced turbulence (VIT). The emission factors (EFs) of all pollutants estimated from conventional two-point measurements varied significantly among arbitrarily chosen pairs of measurement locations (with a range of 53–193% compared to the mean value). These findings suggest the need for multipoint monitoring for more generalizable EF estimates. Our results indicate that installing a sensor array in a tunnel could enable long-term continuous multipoint monitoring of traffic-related pollutants with limited resources. The EF estimates obtained in this study were higher (∼7 times) than those calculated from an emission inventory based on actual traffic data, particularly for CO and wintertime NOX. One possible explanation for this inconsistency is an underestimation of high emitters in the emission inventory. We also estimated VIT as a function of the total traffic flow rate and the fraction of heavy-duty diesel vehicles in a fleet. We expect our results to be used as input data for local and regional air quality models, which can improve the model's performance. We also expect that long-term continuous monitoring of air pollutants in tunnels with multiple sensor nodes will aid evaluations of the effectiveness of air pollution reduction policies by tracking changes in traffic emissions.

Accurate identification of key odorants has become a challenging and urgent task due to the increasing severity of odor nuisance. This study aims to rapidly and accurately identify 20 key odorants in different rubber product industries and treatment procedures using a combination of sensory evaluation, gas chromatography–olfactory–mass spectrometry (GC-O-MS), statistical analysis, and odor activity value (OAV) analysis. The identified odorants include alcohols, acids, aldehydes, ketones, sulfides, aromatic compounds, alkanes, alkenes, and other compounds. These compounds are categorized as sour, odorous, bitter, rubbery, and foul rubber-like, which are commonly associated with odor pollution in the rubber product industry. An odor wheel specific to rubber product enterprises is developed by combining chemical analysis with sensory evaluation of odorants. This odor wheel can be used for rapid traceability and identification of odor pollution at industrial sites. Results present a novel approach and theoretical framework for the traceability and control of odor pollution in the rubber industry.

Bioaerosol concentrations and sizes in indoor environment are associated with human health. Among various indoor environments, the knowledge of the bioaerosol characteristics in underground garages remains unclear. In this study, both DAPI (4′,6-diamidino-2-phenylindole) staining and culturable methods were performed to determine the concentrations and size distributions of bioaerosols in two typical underground garages (an office building (UBOB) and a commercial building (UBCB)) in Xi'an, China. Results indicated that bioaerosol concentrations showed seasonal variations in two garages. The total bioaerosol concentrations were higher in summer (UBOB: (1.07 ± 0.29) ✕105 cells/m3; UBCB: (0.59 ± 0.23) ✕105 cells/m3) than winter (UBOB: (0.34 ± 0.17) ✕105 cells/m3; UGCB: (0.38 ± 0.15)✕105 cells/m3). Higher concentrations of bioaerosols were also found during the rush hour than other times due to the elevated traffic volumes. The spatial distribution of bioaerosols was relatively even for most sampling sites in two garages. Moreover, over 80% of airborne culturable fungi and over 65% of airborne culturable bacteria were in respirable size range (<4.7 μm). Importantly, airborne culturable fungal concentrations in two garages exceeded the proposed thresholds by the World Health Organization (WHO) (500 CFU/m3), with the exceeding standard rate (ESR) of 80.28% and 82.60%, respectively, at UGOB and UGCB, implying that necessary control measures should be taken to reduce microorganism contamination in the air of underground garages. The present results may provide the scientific basis for better understanding pollution characteristics of bioaerosols and formulating prevention and control measures in underground garages.

Accurate long-term (6–24 h) prediction of PM2.5 is critical to human health and daily life. While deep learning techniques have been extensively used to forecast PM2.5, prior studies have primarily relied on shallow recurrent neural networks (RNNs), which may accumulate errors and limit the long-term prediction capability of the model. To address this issue, a new hybrid model has been proposed in this study, which combines the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method with a deep Transformer neural network (DeepTransformer) to enhance the accuracy of long-term PM2.5 forecasting. The model includes a new embedding layer that efficiently models historical, meteorological, and discrete-time data. Additionally, to improve the long-term inference capability of DeepTransformer, a non-autoregressive direct multi-step (DMS) prediction strategy is introduced, and a novel DMS decoder replaces the vanilla Transformer decoder. Experiments conducted on two public datasets demonstrate that the novel model achieves excellent prediction performance. Specifically, DeepTransformer achieves R2=0.984 and RMSE=11.61 µg/m3 in 1-hour prediction and R2=0.704 and RMSE=30.78 µg/m3 in 24-hour prediction. Compared to single models, DeepTransformer achieves a 30% decrease in MAE, a 27% decrease in RMSE, and a 59% increase in R2 for the long-term (24-hour) prediction of PM2.5

In previous studies of pollutant dispersion around street canyons, researchers have found that pollutants do not dissipate easily under stable conditions, but they have tended to study only under weak stability conditions and relatively little research has been done on strong stability conditions. In this study, we mainly used 3-D computational fluid dynamics (CFD) technology to investigate the influence of various Richardson numbers (Rib) on the flow field structure and pollutant dispersion around an ideal street canyon under stable stratification. The results show that under stable stratification, different Rib values have significant effects on the flow field, turbulent field, and concentration field around the street canyon. The main effects are that as Rib increases, the vortices inside the street canyon gradually become larger and move downward, the length of the attachment point behind the buildings first increases and then gradually decreases, and the normalized turbulent kinetic energy (TKE/uH2) gradually decreases. The vertical diffusion range of the plume decreases, the horizontal transport distance increases, and the maximum concentration value at the center of the plume gradually increases. This research result may provide new scientific basis for the prediction and control of urban air pollution.

In this study we investigated the concentration and composition of particulate matter (PM2.5) in the New York City subway system. Realtime measurements, at a 1-s cadence, and gravimetric measurements were performed inside train cars along 300 km of nine subway lines, as well as on 333 platforms on 287 subway stations. The mean (±SD) PM2.5 concentration on the underground platforms was 142 ± 69 μg/m3 versus 29 ± 20 μg/m3 for aboveground stations. The average concentrations inside train cars were 88 ± 14 μg/m3 when traveling through underground tunnels and platforms and 29 ± 31 μg/m3 while on aboveground tracks. The particle composition analysis of filtered samples was done using X-ray fluorescence (XRF), revealing that iron made up approximately 43% of the total PM2.5 mass on station platforms, approximately 126 times higher than the outdoor ambient iron concentration. Other trace elements include silicon, sulfur, copper, nickel, aluminum, calcium, barium, and manganese. Considering the very high iron content, the comparative analysis of the measured concentration versus the standards set by the Environmental Protection Agency (US EPA) is not appropriate since those limits are largely based on particulate matter from fossil fuel combustion. Health impact analysis of inhalation of iron-based particles is needed to contextualize the results presented here.

For human health, tropospheric ozone (O3), particles (PM2.5 and PM10, particles with aerodynamic diameter <2.5 and 10 μm), and nitrogen dioxide (NO2) are the most harmful air pollutants. We have investigated the simultaneity of PM2.5, PM10, NO2, and O3 occurrence by using data from 21 ground-based monitoring stations in a megacity of the Middle East, Tehran (Iran), between 2011 and 2022. In Tehran, the daily PM2.5 (21–45 μg m−3), PM10 (52–108 μg m−3) and NO2 (75–146 μg m−3) mean concentrations have largely exceeded the 24-h limit value established by the 2021 World Health Organization for the protection of human health, i.e., 15, 45 and 25 μg m−3, respectively, in particular for NO2. The ground-level daily O3 concentrations ranged from 26 to 47 μg m−3. Changes in aerosols burden (e.g., PM2.5 and PM10) affect surface O3 through changes in aerosol chemistry and photolysis rates. Following the dust events and for the days with PM2.5 and PM10 exceeding 50 and 100 μg m−3, respectively, we observed a reduction of surface O3 levels (- 30.0% and - 13.5%), concurrently to surface NO2 (+27.9% and +16.6%) levels increases compared to the non-dusty clear-sky days over Tehran city. Surface O3 formation is suppressed under high particulate matter (PM) levels in the atmosphere, in particular PM2.5, likely due to weakened photochemical reactions (lower solar radiation and air temperature), dilution effect, and heterogeneous chemical processes occurring onto the PM2.5 surface (reactive uptake of nitrogen oxides, NOx, and hydrogen oxide radicals, HOx). To achieve O3-PM co-improvement in cities, we recommend reductions in NOx emissions concurrently with significant reductions in PM and Volatile Organic Compounds emissions, for instance by suitable greening and re-naturing programs.

Researchers have studied motorcyclists' exposure to Particulate Matter (PM) during rush and non-rush hours. However, the combined effect of season and hour of the day on PM concentration has not been studied. PM concentration was measured near the typical breathing zone of motorcyclists who traveled along four designated routes in a densely populated city (Varanasi, India). Data were collected from January to June 2022 during various hours of the day (from 07:00 to 17:00 h). PM2.5 and PM10 concentration during winter was 2.36 and 1.69 times in summer, respectively. In contrast, PM2.5 and PM10 concentration during spring were 1.35 and 1.12 times during summer, respectively. PM2.5 correlated much more with relative humidity and atmospheric temperature than PM10. Higher PM concentration was recorded in rush hour (09:00–10:00) during spring and summer but in non-rush hours (12:00–13:00) during winter. The opposite trend in the winter was caused by more extended dispersion and dilution time of PM particles. Also, a higher proportion of PM2.5 was observed on routes with longer rush hours caused due to road encroachment by street vendors and pedestrians.

In this study, we described the spatial asymmetry of the Asian Summer Monsoon Anticyclone (ASMA) circulation using Geopotential Height (GPH) values and divided ASMA into North-West (NW: 32.5°–37.5° N, 40°-70°E), North-East (NE: 32.5°–37.5° N, 70°-100°E), South-West (SW: 27.5°–32.5° N, 40°-70°E) and South-East (SE: 27.5°–32.5° N, 70°-100°E) regions. We provided the spatial asymmetry in the aerosol distribution using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) (2006–2020) measurements. The vertical distribution of aerosols has been investigated over these regions dividing the atmosphere into (1) Boundary Layer-BL (0–1.5 km), (2) Mid-Troposphere-MT (∼1.5–8 km) and (3) Upper Troposphere and Lower Stratosphere -UTLS (∼8–18 km). Highest aerosol extinction values are noticed in the MT and their contribution to the total Aerosol Optical Depth (AOD) is highest (∼50–80%). The contribution of UTLS aerosols to the total AOD is ∼10% particularly in the eastern part (NE/SE) of the ASMA. The enhancement of aerosols throughout the upper troposphere over the eastern regions (SE/NE) of ASMA, indicates the possible transport of boundary layer pollutants to the UTLS. Using cloud fraction measurements from CloudSat and Pressure vertical velocity (ω), we identified that the intense convection over Bay of Bengal (BoB) is responsible for the UTLS aerosols over the SE region of ASMA. The descending limb of the monsoon-induced circulation over the western region of ASMA (Arabian Peninsula and other Middle Eastern countries) is the possible causative mechanism for the removal of aerosols in the upper troposphere. These findings on the spatial asymmetry in the aerosol distribution over the ASMA are expected to provide the importance of the regional radiative forcing and their climatic impacts.

Methane (CH4), a greenhouse gas with significant global warming potential, has complicate spatial and temporal distributions, especially for area with intense anthropogenic and natural sources. Here, we present the atmospheric CH4 record from three stations in Suzhou, China, which is located in the famous economic developed zone, with wetland coverage about 20%. The study found that although significant variations in CH4 concentrations across different regions within the Suzhou city, the mean values of the three sites could represent the atmospheric CH4 levels in Suzhou. The annual mean CH4 value in 2021 was significantly higher than that in 2020, with a growth of 8.02 ppb yr−1. CH4 followed a seasonal pattern, with low values in the spring and winter, and peak values in the autumn and summer. However, there were trough and surge values over time, which occurred in summer and winter in both year. These results highlighted that huge amounts of OH radicals accumulated when the summer extended a period of drought and heat, aggravating CH4 consumption and lowering concentration. Moreover, methanogens in subtropical areas might not be impacted by winter's lower temperatures. The CH4 significantly decreased exponentially with the increased wind speed, positively correlated with air temperature in spring and negatively correlated with atmospheric pressure. Taihu Lake, lied in the WNW, W, WSW and SW winds sectors with high concentration of CH4, is a local source. The Yellow Sea and the East China Sea where have some distance from Suzhou are important regional sources to the CH4.

Intensive field observations were conducted on the PM2.5, gaseous pollutants, δ13C, and δ15N values to evaluate the efficacy of much stricter air quality measures and emergency response strategies implemented in Hangzhou and Ningbo during the 2016 G20 Summit. Our data showed that δ13C values of PM2.5 samples collected in two cities varied from −30.8‰ to −24.2‰, with an average of −25.8‰ ± 1.1‰ during sampling periods. Similar δ13C values observed in the two cities indicated that there is no pronounced differences of carbon sources between the Hangzhou and Ningbo. Based on the Bayesian model, results indicated that C3 plant combustion contributed 47.8% and 47.2% to carbon sources of PM2.5 in Hangzhou and Ningbo, respectively. δ15N values of PM2.5 ranged from 2.0‰ to 13.7‰, with a mean value of 8.5‰ ± 2.5‰ during the whole campaign. Notably, more effective results were observed in reducing NH3 compared to NOx in Hangzhou and Ningbo. Additionally, a more reliable source apportionment approach using isotope fractionation effects and a Bayesian model was applied (improved Bayesian model), which reduced the uncertainty in the apportionment of nitrogen sources. The estimated fractionation effects from NH3 to NH4+ averaged at 16.19‰ ± 3.49‰, and from NOx to NO3− averaged at 12.94‰ ± 2.60‰, respectively. Compared to the results of the Bayesian model, the contributions of NOx from coal combustion (13.8%) and vehicle exhausts (11.6%), and NH3 from coal combustion (12.1%) and vehicle exhausts (12.5%) by improved Bayesian model were more consistent with the emission inventory. Collective evidence of multiple isotopes indicated that pollution emissions were sensitive in response to strict control measures. Our results also revealed that fossil fuel combustion reduction and regional control policies should be synergistically implemented for better air quality.

Particle exposure affects more humans globally than any other air pollutant. However, due to expensive instruments and infrastructural deficiency, a high spatiotemporal network of monitoring stations is not possible, leading to data-scarce regions. Satellite and reanalysis datasets can be implemented to estimate particulate matter, but they do not provide surface concentration and needs to be reconstructed from the components. In this study, a machine learning (ML) framework is implemented to reconstruct PM2.5 from MERRA-2 data components, namely black carbon (BC), organic carbon (OC), dust (DUST), sea salt (SS), and sulfate (SO4) mass concentration. The ground-level data were collected from India's 335 continuous ambient air quality monitoring stations (CAAQMS) and respective MERRA-2 data for 2017–2021 at hourly resolution. Random forest (RF) performs better with train and test scores (R2) of 0.84 and 0.73, respectively, while the empirical equation provides an R2 of only 0.26 on test data. The estimated PM2.5 for Indian states from 1980 to 2021 indicates a significant increase in most cases. However, states in the Indo-Gangetic plain such as Delhi, Punjab, Haryana, and Uttar Pradesh, are the most polluted regions of India. The major shift in concentration is from 2000 onwards, which can be seen as a direct result of the economic liberalization policies implemented in 1991. The results provide evidence for the limitations of the broad application of the empirical equation and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific models from MERRA-2 data.

Formaldehyde (HCHO) plays an important role in the troposphere, as it is a major precursor of ozone and heavily affects the health of lives on the earth. However, to the present, major factors that influence the diurnal variation of HCHO in the Yangtze River Delta (YRD) of China have not been investigated thoroughly. Moreover, sensitivities of the HCHO concentration in cities of YRD to the primary anthropogenic emissions of these cities are also unclear. Thus, we used a mesoscale meteorological model coupled with chemistry, WRF-Chem to capture the variation of HCHO in ten cities of YRD, so that the factors dominating the diurnal change of HCHO in these cities can be identified. We also performed sensitivity tests by varying the primary anthropogenic emission intensity of HCHO, to investigate the sensitivity of HCHO in each city to primary anthropogenic emissions of HCHO. The simulation results showed that the driving factors for the daytime peak of HCHO include photochemical reactions and VOCs＋OH reactions. The change in meteorological conditions causes a deviation of the temporal evolution of HCHO in many cities of YRD from the conventional trend (i.e., peak at noon and low in the rest). The major impact areas of HCHO anthropogenic emissions from the cities of YRD and the contributions to HCHO in these cities were also revealed.

The present study aims to investigate the effect of forests on PM distribution following dust events in a region prone to frequent dust storms (Northern Negev, Israel). 3D numerical modeling of the particulate flow using a discrete phase model, based on the Eulerian-Lagrangian approach, has been performed to investigate the dust deposition to the vegetation element. The modeling considered several deposition mechanisms, namely, the drag force, buoyancy force, Brownian force, and Saffman's lift force. The vegetation element is considered as a prolate ellipsoid approximating in shape to a leaf of Aleppo pine (Pinus halepensis), the dominant tree species in Lahav Forest. Based on CFD modeling, the novel parameterization of the average collection rate is suggested. The validity of the model prediction is evaluated by comparison with experimental data available in the literature. It is shown that using a correlation based on dimensionless mass transfer parameters leads to an overestimation of the dust concentration at the leeward side of the forest by 23% compared to the measurement data. By contrast, the model supplemented with CFD-based average collection rate parameterization yields an overestimation of only 8%. It was also found that both models estimate the dust concentration with a difference of less than 5% in a location inside the forest in the area adjacent to the windward side of the forest. The influences of other essential factors, such as particle size and wind velocity, on deposition efficiency and the deposition velocity of dust particles on the vegetation element, are also analyzed and discussed.

In response to the greenhouse effect, 178 countries (regions) around the world have signed “the Paris Agreement” to combat climate change. As the world's second largest source of carbon emissions, the transport sector is in urgent need of “green transformation”. China is working to reduce carbon emissions from transport by developing a new energy vehicle (NEV) industry. In order to ensure the accurate formulation and promotion of government policies, accurate prediction of NEV ownership is crucial. To this end, this study developed a combined model based on grey relational analysis and bi-directional long- and short-term memory (GRA-BiLSTM). Firstly, GRA was used to evaluate and screen the experimental data indicators that affect NEV retention. Secondly, BiLSTM model was used to learn the characteristics of important impact indicators. The mean absolute percentage error (MAPE) of the GRA-BiLSTM combined model established in this study is 5.16%, which is lower than the other seven comparative prediction models. Then, three development scenarios of low, medium, and high are set to predict the new energy vehicle ownership in China from 2020 to 2030 and calculate the carbon emission reduction. The results show that in the three development scenarios of low, medium and high, the new energy vehicle ownership develops to 35,228.08, 51,865.48 and 71,887.82 thousand vehicles in 2030, respectively, and the calculated carbon emission reduction quantities are 3433835.63 Metric Tons, 4600719.93 Metric Tons, and 5837315.76 Metric Tons, respectively. Finally, the NEV retention and carbon emission reductions for 2031–2060 are projected based on the average development scenarios.

The present study investigated the possible effects of aerosols on cloud properties during positive and negative Indian Ocean Dipole (IOD) months over South Asia using 20 years (2002–2021) of long-term satellite-based observations and reanalyses datasets. Positive aerosol optical depth (AOD) differences between the two IOD phases are found mainly over the land areas and some parts of the Indian Ocean, with the maximum value observed over Indonesia, Malaysia, Northern China, Western India, and the Northern Arabian Sea. The negative AOD differences are seen over the equatorial Indian Ocean, some regions of Indian land mass, the North Indian Ocean, and some parts of the South China Sea, with the highest dip over the central Indo-Gangetic Plain region. The highest increase (decrease) in rainfall is observed over the western (eastern) pole. The reduced rainfall is responsible for the increased AOD over Indonesia, Malaysia, and neighboring regions. The strong negative relationships of cloud fraction (CF) with cloud top pressure and cloud optical depth (COD) suggest persisting optically thick middle and high-level clouds during the IOD months. Twomey effect is noticed over the study region during both the IOD phases irrespective of the meteorological conditions. CF and COD both showed an upsurge with increased AOD in the presence of high relative humidity and stronger updrafts, suggesting the role of prevailing meteorology in forming thicker clouds. However, the height of the cloud tops is getting lowered in polluted conditions. The aerosol-cloud relationships observed during IOD scenarios could be attributed to the prevailing meteorology.

Recent studies show that aerosols are highly linked to the spread of the COVID−19 pandemic. Furthermore, during this pandemic, the largest Saharan dust intrusion event has reached the Caribbean region in the last 20 years, called “Godzilla” African Dust or GAD. This study aims to analyze the correlation between the spread of COVID−19 and the GAD event in the main cities of the Colombian Caribbean region. The results showed a positive correlation between the spread of COVID−19 and the GAD event in most cities. Our findings could serve as input for the development of a strategy in the prevention of COVID−19 and other similar viral diseases during the Saharan dust intrusion events that reach the Caribbean region each year from Africa. Our results may help design strategies to prevent future outbreaks of COVID-19 and reduce the risk of future pandemics of similar viral diseases. Especially during the Saharan dust intrusion events that reach the Caribbean region each year.