Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India.

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 µg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 µg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

"I Think You Covered the Three Levels of Drugs and Consent": Qualitatively Testing Different Operationalizations of an Alcohol and Other Drugs-Involved Sexual Violence.

Substance-involved rape is increasing among college students, particularly women (Koss et al., 2022). Addressing rape requires first measuring it accurately in surveys to understand its true scope and nature. We used cognitive interviews with 40 young adults to qualitatively test the construct validity of an alcohol- and other drugs (AOD)-involved rape item in the Sexual Experiences Survey by asking participants to comment on different operationalizations of this construct. Our findings revealed that different phrasings elicited different interpretations of the items by participants. Specifically, the results indicated that (1) respondents viewed the different operationalizations as a sequence of events with varying severity; (2) some participants focused on the intentionality and responsibility of the perpetrator as opposed to opportunistic perpetration; and (3) study participants consistently chose one of the operationalizations as describing "being roofied" (being drugged without consent). Participants also contributed additional scenarios not described in the questionnaire and shared their interpretations of the items. The results underscore the importance of refining survey language to properly measure AOD-involved rape and allow us to understand how to tailor appropriate questions for best comprehension. The findings indicate the benefit in including several items about AOD-involved rape in questionnaires such as the Sexual Experiences Survey, with each item addressing different scenarios of victim intoxication. The results could also have important implications for sexual violence prevention programs, which should discuss consent, intentions, and responsibility specifically in the context of AOD consumption.

Research and Implementation of Indoor Positioning Algorithm Based on Bluetooth 5.1 AOA and AOD.

With the addition of Bluetooth AOA/AOD direction-finding capabilities in the Bluetooth 5.1 protocol and the introduction of antenna array technology into the Bluetooth platform to further enhance positioning accuracy, Bluetooth has gradually become a research hotspot in the field of indoor positioning due to its standard protocol specifications, rich application ecosystem, and outstanding advantages such as low power consumption and low cost compared to other indoor positioning technologies. However, current indoor positioning based on Bluetooth AOA/AOD suffers from overly simplistic core algorithm implementations. When facing different application scenarios, the standalone AOA or AOD algorithms exhibit weak applicability, and they also encounter challenges such as poor positioning accuracy, insufficient real-time performance, and significant effects of multipath propagation. These existing problems and deficiencies render Bluetooth lacking an efficient implementation solution for indoor positioning. Therefore, this paper proposes a study on Bluetooth AOA and AOD indoor positioning algorithms. Through an analysis of the principles of Bluetooth's newly added direction-finding functionality and combined with research on array signal DOA estimation algorithms, the paper ultimately integrates the least squares algorithm to optimize positioning errors in terms of accuracy and incorporates an anti-multipath interference algorithm to address the impacts of multipath effects in different scenarios. Experimental testing demonstrates that the indoor positioning algorithms applicable to Bluetooth AOA and AOD can effectively mitigate accuracy errors and overcome multipath effects, exhibiting strong applicability and significant improvements in real-time performance.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Interactions between aerosols and surface ozone in arid and semi-arid regions of China.

Atmospheric aerosols affect surface ozone concentrations by influencing radiation, but the mechanism and dominant factors are unclear. Therefore, this paper analyses the changes in aerosol-radiative-surface ozone in China's arid and semi-arid regions with the help of the Atmospheric Radiative Transfer (SBDART) model. The results suggest that Aerosol Optical Depth (AOD) and coarse Particulate Matter (PM10) have the same trend, with high values in spring and winter and low values in summer and autumn. Surface ozone is high in spring and summer and low in autumn and winter. Surface ozone is higher in spring and summer and lower in autumn and winter. In winter, mainly secondary pollutants are dominated by high pollution levels. In the rest of the seasons, a mixture of dust, motor vehicle exhaust, and soot is dominated by low pollution levels. Surface ozone is positively correlated with fine particles and negatively correlated with coarse particles. Temperature is positively correlated with surface ozone in all seasons and negatively correlated with PM10 in summer, autumn, and winter. Precipitation negatively correlates with PM10 each season and surface ozone in winter and spring. Analysis of surface ozone and PM10 sources in the more polluted city of Hohhot based on the back-line trajectory model showed that airflow trajectories mainly transported surface ozone and PM10 pollution from northwestern Inner Mongolia and western Mongolia. During dusty solid weather, the decrease in radiation reaching the Earth's surface and the cooling effect of aerosols lead to lower temperatures, which slows down the rate of chemical reactions of precursors of surface ozone, resulting in lower ozone concentrations at the surface. This study can provide a theoretical reference for aerosol and surface ozone control in arid and semi-arid areas of China.

Successful closure of transcatheter aortic valve replacement-induced Gerbode defect with valve-in-valve technique: A case report.

We present the first documented case of a successful closure of a transcatheter aortic valve replacement (TAVR)-induced Gerbode defect using a valve-in-valve approach. A 90-year-old female with severe aortic stenosis underwent TAVR. Following post-dilatation, the patient experienced hemodynamic deterioration and collapse due to tamponade and sub-annular rupture leading to hemodynamic deterioration and the development of a Gerbode defect with communication between the left ventricle and right atrium. Hemodynamic stabilization was achieved through pericardiocentesis, followed by the low implantation of a second valve, effectively sealing the rupture. This case showcases a valuable alternative for managing rare challenging complications during TAVR procedures.

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India.

Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 µg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction.

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R2 is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM2.5 predictions. In addition, it evaluates different machine learning approaches in PM2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis.

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain.

Indo-Gangetic Plains (IGP) experiences high loading of particulate and gaseous pollutants all year around and is considered to be the most polluted regions of India. Understanding the effect of landscape determinants on air pollution in IGP regions is crucial to make its environment sustainable. We examined satellite retrievals of OMI NO2 and SO2, and MODIS AOD to analyse the long-term trend, spatio-seasonal pattern and dynamics of aerosols, NO2 and SO2 over three IGP regions, namely Upper Indo-Gangetic plain (UIGP), Middle Indo-Gangetic plain (MIGP) and Lower Indo-Gangetic plain (LIGP) over the period 2005-2019. IGP experienced an overall increment in AOD (R2 = 0.63) and SO2 (R2 = 0.67) values, with LIGP (AOD, R2 = 0.8 & SO2, R2 = 0.8) experiencing the largest rate of enhancement. The levels of NO2 (R2 = 0.2) experienced a decrement after 2012 (owing to implementation of vehicle emission policy) except in MIGP, with UIGP (R2 = 0.23) exhibiting the largest rate of decrement. Seasonal heterogeneity in the nature of sources was observed over IGP regions. AOD (0.61 ± 0.1) and NO2 value (3.82 ± 0.98 × 1015 molecules/cm2) were found highest during post-monsoon in UIGP owing to crop residue burning activity. The value of NO2 (3.8 ± 1.4 × 1015 molecules/cm2) in MIGP was found highest during pre-monsoon due to high consumption of coal in power plants for summer cooling demand. The highest SO2 level (0.09 ± 0.06 DU) was observed during post-monsoon in UIGP, as a large number of brick kilns are fired during this period. Correlations among landscape determinants and pollutants revealed that topography is the dominant variable that affect the spatial pattern of AOD compared to vegetation and land use. Lower elevation tends to have high AOD values compared to higher elevation. Vegetation-AOD relationship showed an inverse association in IGP regions and is influenced by factors such as seasonal meteorology and size of the airborne particles. Vegetation possesses positive relationship with SO2 and NO2, implying no pollution abatement effect on SO2 and NO2 pollutants. Built-up change has deteriorating effect as well as quenching effect on pollutants. Increase in built terrain have deteriorated the air quality in UIGP whereas it favored in suppressing the aerosol level in LIGP.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak.

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

High-Yield Preparation of American Oyster Defensin (AOD) via a Small and Acidic Fusion Tag and Its Functional Characterization.

The marine peptide, American oyster defensin (AOD), is derived from Crassostrea virginica and exhibits a potent bactericidal effect. However, recombinant preparation has not been achieved due to the high charge and hydrophobicity. Although the traditional fusion tags such as Trx and SUMO shield the effects of target peptides on the host, their large molecular weight (12-20 kDa) leads to the yields lower than 20% of the fusion protein. In this study, a short and acidic fusion tag was employed with a compact structure of only 1 kDa. Following 72 h of induction in a 5 L fermenter, the supernatant exhibited a total protein concentration of 587 mg/L. The recombinant AOD was subsequently purified through affinity chromatography and enterokinase cleavage, resulting in the final yield of 216 mg/L and a purity exceeding 93%. The minimum inhibitory concentrations (MICs) of AOD against Staphylococcus aureus, Staphylococcus epidermidis, and Streptococcus galactis ranged from 4 to 8 µg/mL. Moreover, time-killing curves indicated that AOD achieved a bactericidal rate of 99.9% against the clinical strain S. epidermidis G-81 within 0.5 h at concentrations of 2× and 4× MIC. Additionally, the activity of AOD was unchanged after treatment with artificial gastric fluid and intestinal fluid for 4 h. Biocompatibility testing demonstrated that AOD, at a concentration of 128 µg/mL, exhibited a hemolysis rate of less than 0.5% and a cell survival rate of over 83%. Furthermore, AOD's in vivo therapeutic efficacy against mouse subcutaneous abscess revealed its capability to restrain bacterial proliferation and reduce bacterial load, surpassing that of antibiotic lincomycin. These findings indicate AOD's potential for clinical usage.

Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020.

Monitoring long-term variations in fine particulate matter (PM2.5) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM2.5 concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM2.5 measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM2.5 hindcast modeling framework to generate the full-coverage, daily, 1-km PM2.5 data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM2.5 estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.90 and 12.94 µg/m3 and significantly improved the model performance in years without PM2.5 measurements, raising the leave-one-year-out CV R2 [RMSE] to 0.83 [12.10 µg/m3] at a monthly scale (0.65 [23.29 µg/m3] at a daily scale). Our long-term PM2.5 estimates show a sharp decline in PM2.5 exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM2.5 in China.

Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt.

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of - 0.735, and both have a moderate association with AOD with a correlation of 0.451 and - 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran.

High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula: see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula: see text]) as well as classic data-driven methods such as random forest ([Formula: see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.

'We have goals but [it is difficult]'. Barriers to antiretroviral therapy adherence among women using alcohol and other drugs living with HIV in South Africa.

BACKGROUND: Women living with HIV who misuse alcohol and live in economically disadvantaged settings in South Africa experience a multitude of contextual barriers as they navigate the HIV care continuum. The Women's Health CoOp (WHC), a brief, woman-focused, behavioural, evidence-based intervention, has been shown to be effective in reducing heavy drinking and improving HIV-related outcomes among this key population. However, these women face other broader socioecological barriers to antiretroviral therapy (ART) adherence. METHODS: The WHC was implemented in a modified, stepped-wedge implementation science trial in public health clinics and substance use treatment programmes in Cape Town, South Africa. A qualitative substudy was conducted to explore barriers to HIV treatment adherence among women enrolled in this trial. Eight focus group discussions were conducted with 69 participants 6 months after completion of the WHC workshops. Focus groups were audio-recorded (with consent), transcribed verbatim and analysed using a thematic approach. RESULTS: The mean age of the participants was 33 years and the mean self-reported number of drinks per day was 13. The main contextual factors influencing participants' ART adherence were intrapersonal-level factors (substance use, financial constraints, food insecurity; community-level factors (anticipated and enacted stigma, community violence) and institutional-level factors (patient-provider relationships, health facility barriers, environmental stigma). CONCLUSION: Comprehensive interventions addressing the contextual barriers and unique challenges faced by women who misuse alcohol in low-resource settings that intersect with HIV treatment nonadherence should be implemented in tandem with successful biobehavioural HIV interventions for long-term effectiveness and sustainability. PATIENT OR PUBLIC CONTRIBUTION: Our South African community collaborative board has been involved throughout this study; participants and clinic staff voices have been essential in our interpretation of these findings.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California.

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 µg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 µg/m3) and 0.73 (16.58 µg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 µg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 µg/m3) and 0.79 (11.89 µg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 µg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 µg/m3) and 0.85 (14.88 µg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Interaction of aerosol with meteorological parameters and its effect on the cash crop in the Vidarbha region of Maharashtra, India.

Regional weather variability depends on various meteorological variables such as temperature and rainfall. The current research focuses on the variability and trends in annual aerosol optical depth (AOD), temperature (T), and rainfall (RF) in 11 Vidarbha districts. The annual trend analysis of AOD, T, and R is determined using the non-parametric Sen slope and Mann-Kendall (MK) test at a 5% significant level from 1980 to 2019. Annual T and AOD indicate a substantial increase in this study, whereas rainfall shows a non-significant trend (MK, test) over the study period. According to Sen's slope trends, the relatively high rainfall area (Chandrapur = 1.273 and Garchiroli = 4.06) got positive trends, but Gondia and Bhandara districts have negative (Sen's slope = - 2.79 and - 2.56) trends. The moderate rainfall areas are showing a less negative Sen slope (Wardha = - 0.21, Washim = - 1.13 and Yavatmal = - 2.75), whereas Nagpur districts' Sen's slope shows a positive value (Sens's slope = 0.72). The assured rainfall area districts show Sen's slope trends are positive (Akola = 0.45, Amravati = 1.17 and Buldana = 0.42). Sen's slope trend indicates rising rainfall, whereas negative trends indicate decreasing rainfall in the time series. This study has also looked at the effect of RF, AOD, and T on the last two decades' cash crop production (2000-2019) for Vidarbha districts. The relationship between rainfall departure (DRF) and cash crop yield has also been highlighted. Five cash crops, such as cotton (Ct), total cereals (TCrl), total oilseeds (TOsd), total pulses (TPS), and sugarcane (Sc), are selected for the present study.

Simultaneous Extraction of Planetary Boundary-Layer Height and Aerosol Optical Properties from Coherent Doppler Wind Lidar.

Planetary boundary-layer height is an important physical quantity for weather forecasting models and atmosphere environment assessment. A method of simultaneously extracting the surface-layer height (SLH), mixed-layer height (MLH), and aerosol optical properties, which include aerosol extinction coefficient (AEC) and aerosol optical depth (AOD), based on the signal-to-noise ratio (SNR) of the same coherent Doppler wind lidar (CDWL) is proposed. The method employs wavelet covariance transform to locate the SLH and MLH using the local maximum positions and an automatic algorithm of dilation operation. AEC and AOD are determined by the fitting curve using the SNR equation. Furthermore, the method demonstrates the influential mechanism of optical properties on the SLH and MLH. MLH is linearly correlated with AEC and AOD because of solar heating increasing. The results were verified by the data of an ocean island site in China.