A 13-year space-based climatological evolution of desert dust aerosols in the Middle East and North Africa regions observed by the CALIPSO.

The present study explores the intricacies of CALIPSO Level 3 optimized Aerosol Optical Depth (AOD) and Dust Aerosol Optical Depth (DAOD) products. Hence, the study focused on regions in the Middle East and North Africa (MENA) across different seasons from January 2007 to December 2020. The study utilizes a refined 1°â€¯× 1° grid resolution to analyze horizontal distribution patterns, seasonal variations, and the interplay of various aerosol constituents. The Middle East (ME) stands out with intensified AOD during transitional periods, and the Saharan-Sahel Dust (SSD) belt exhibits higher DAOD during specific seasons. Regions with significant industrialization and human activities exhibit high non-dust AOD values, while major dust sources like the SSD and the Arabian Desert showed high DAOD values in the spring and summer seasons. The study reveals seasonal variations in AOD and DAOD, with different regions showing distinct characteristics influenced by topographic and environmental factors. Observational evidence on the vertical distribution of dust layers is crucial for modeling studies to assess the impact of airborne dust particles on radiation and clouds. However, there are challenges in assimilating dust into atmospheric models due to limited ground measurements near dust sources. Further, the statistical metrics highlight regional and seasonal variations in DAOD, Dust Center of Mass, and Dust Top Height. The analysis extends to particle depolarization ratio, aerosol classification, spatial deviation in dust composition, AOD, and cloud properties (e.g., cloud optical thickness and cloud fraction). This has been influenced by several factors such as atmospheric circulation patterns, temperature, humidity, and land cover changes. Trends in AOD and DAOD over timescale indicate regional variations in aerosol concentrations. The study offers valuable insights into the complex atmospheric phenomena shaping the examined regions over the 13 years.

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.

Understanding cultural inclusion in alcohol and other drug services in New South Wales, Australia and assessing the acceptability of a cultural inclusion audit.

INTRODUCTION: Cultural inclusion and competence are understood at the most basic level to be the practice of considering culture so as to provide effective services to people of different cultural backgrounds. In order to work better with clients from diverse backgrounds, alcohol and other drug (AOD) services need to offer a service that is designed to be accessible to all people, where systems in place operate in a way that considers different cultural needs. This research aimed to assess the extent to which non-government AOD services in New South Wales are positioned to support cultural inclusion as well as to evaluate the acceptability of a cultural inclusion audit across four AOD sites. METHODS: The research adopted a mixed methods approach comprising of a pre-audit online survey (n = 85) designed to assess AOD services' attitudes and practices towards cultural inclusion, and in-depth interviews that were conducted with nine AOD service staff and four cultural auditors to explore the acceptability of a cultural inclusion audit process. RESULTS: Findings from the survey indicate cultural inclusion practices are limited. Interview data highlight that while staff are not fully aware of what appropriate cultural inclusions entails, they are receptive to and want a cultural inclusion program. DISCUSSION AND CONCLUSIONS: The study illustrates the benefits of implementing a cultural inclusion audit process aimed at raising awareness of what cultural inclusion entails. Including a cultural inclusion service audit is likely to enhance AOD service provision to culturally and linguistically diverse groups and thereby improve treatment outcomes.

The risk of repeated suicidal presentations following residential treatment for substance use disorders: A recurrent event analysis using linked administrative data.

BACKGROUND: Individuals seeking alcohol and other drug (AOD) treatment consistently experience higher rates of suicidal behaviours and death by suicide when compared to the general population. By linking residential AOD treatment data to administrative healthcare and death datasets, we aimed to examine suicide-related behaviours and identify risk and protective factors for these events following discharge from residential treatment. METHODS: Participants included 1056 individuals aged 18-69 (M = 32.06, SD = 9.55, male = 696,65.9 %) admitted to three residential treatment facilities in Queensland, Australia from January 1, 2014 to December 31, 2016. Treatment data was linked to administrative hospital, emergency department (ED), mental health service, and Registry of Deaths data 2-years post-discharge. ICD-10 codes were used to identify and analyse suicide-related events. RESULTS: Within 2-years post-discharge, 175 (16.6 %) individuals had a suicide-related event (n = 298 episodes). The highest proportion of episodes (11.1 %) occurred within 1-month of discharge. Higher risk of a recurrent suicide-related event was associated with receiving a Disability Support Pension (aHR = 1.69 (95%CI:1.10,2.59), two or more previous episodes of residential AOD treatment (aHR = 1.49 (95%CI:1.30,2.15). Completing residential treatment was associated with a lower risk of suicide-related events (aHR = 0.54 (95%CI:0.35,0.83). LIMITATIONS: The amalgamation of suicidal ideation, attempts, and death into a single outcome oversimplifies their complex nature and interplay. The exclusive focus on one service provider limits generalisability, and data constraints and missingness preclude many analyses. CONCLUSIONS: Understanding suicidal behaviours and critical risk periods following discharge from residential treatment is crucial for improving continuing care, developing effective suicide prevention, and implementing targeted interventions among this high-risk population.

Anterior Segment Changes in Patients With Glaucoma Following Cataract Surgery.

This prospective observational study investigates the impact of cataract surgery on anterior segment parameters in nonglaucomatous and primary open-angle glaucoma (POAG) eyes, utilizing anterior segment optical coherence tomography (AS-OCT). The study involved 42 Caucasian patients, divided into a control group and a POAG group. Comprehensive ophthalmic examinations were performed along with AS-OCT imaging and biometry preoperatively, as well as on one day, one week, and one month following cataract surgery. The results showed significant post-operative changes in anterior chamber depth (ACD) and angle width in both groups, suggesting that cataract surgery may influence the structural parameters associated with glaucoma risk and management. Specifically, a marked increase in ACD and improvement in angle-opening distances were observed, highlighting the potential of cataract extraction to alter intraocular fluid dynamics favorably. Despite these changes, the study noted an initial spike in increased intraocular pressure (IOP) in POAG patients immediately post-operative, which stabilized during follow-up. For the control group, IOP showed gradually reducing IOP values in the follow-up visits. The findings underscore the role of advanced imaging technologies in understanding glaucoma's pathophysiology and the potential benefits of cataract surgery in glaucoma patients. The study advocates for further research with a larger, more diverse patient population and extended follow-up to explore the long-term implications of cataract surgery on glaucoma dynamics, emphasizing the importance of personalized management and treatment strategies particularly for glaucoma patients.

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.

Dust and climate interactions in the Middle East: Spatio-temporal analysis of aerosol optical depth and climatic variables.

The Middle East (ME) is grappling with an alarming increase in dust levels, measured as aerosol optical depth (AOD), which poses significant threats to air quality, human health, and ecological stability. This study aimed to investigate correlations between climate and non-climate driving factors and AOD in the ME over the last four-decade (1980-2020), based on analysis of three variables: actual evapotranspiration (AET), potential evapotranspiration (PET), and precipitation (P). A comprehensive analysis is conducted to discern patterns and trends, with a particular focus on regions such as Rub al-Khali, Ad-Dahna, An-Nafud Desert, and southern Iraq, where consistently high dust levels were observed. 77 % of the study area is classified as arid or semi-arid based on the aridity index. Our results indicate an upward trend in dust levels in Iraq, Iran, Yemen, and Saudi Arabia. We noted an increasing AET trend in regions such as the Euphrates and Tigris basin, northern-Iran, and the Nile region, along with rising PET levels in arid and semi-arid zones such as Iran, Iraq, and Syria. Conversely, P showed a notable decrease in northern-Iraq, Syria, southwestern Iran, and southern-Turkey. Comparison of long-term changes (10-year moving averages) of AOD and P showed a consistent increase in AOD with P levels decreasing in all climate regions. The Budyko space analysis indicates shifts in evaporation ratio across different climate classes from 1980 to 2020, with predominant movement patterns towards higher aridity indices in arid and semi-arid regions, while factors beyond long-term aridity changes influence shifts in evaporation ratio across various climatic zones. The Middle East experiences complex and intricate interactions between dust events and their drivers. To address this issue, a comprehensive and multi-system approach is necessary, which considers both climate and non-climate drivers. Moreover, an efficient dust control strategy should include soil and water conservation, advanced monitoring, and public awareness campaigns that involve regional and international collaboration.

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

BACKGROUND: Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. OBJECTIVE: This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. METHODS: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. RESULTS: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. IMPACT STATEMENT: We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China.

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

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.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years.

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

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.

Preparation and Mechanism Analysis of Stainless Steel AOD Slag Mixture Base Materials.

To promote resourceful utilization of argon oxygen decarburization (AOD) slag, this research developed a new three-ash stabilized recycled aggregate with AOD slag, cement, fly ash (FA), and recycled aggregate (RA) as raw materials. The AOD slag was adopted as an equal mass replacement for fly ash. The application of this aggregate in a road base layer was investigated in terms of its mechanical properties and mechanistic analysis. First, based on a cement: FA ratio of 1:4, 20 sets of mixed proportion schemes were designed for four kinds of cement dosage and AOD slag replacement rates (R/%). Through compaction tests and the 7-day unconfined compressive strength test, it was found that a 3% cement dosage met the engineering requirements. Then, the unconfined compressive strength test, indirect tensile strength test, compressive rebound modulus test, and expansion rate test were carried out at different age thresholds. The results showed that the mixture's strength, modulus, and expansion rate increased initially and then stabilized with age, while the strength and modulus initially increased and then decreased with increasing R. Secondly, based on X-ray diffraction (XRD) and scanning electron microscopy (SEM) used to analyze the mechanism, it was found that the strength, modulus, and expansion rate of the new material can be promoted by blending AOD slag, due to its ability to fully stimulate the hydration reaction and pozzolanic reaction of the binder. Finally, based on the strength and modulus results, R = 3% was identified as the optimal ratio, which provides a reference point for the effective application of AOD slag and RA in road base materials.

Influence of industrial sustainability transition on air quality in a typical resource-exhausted city.

The industrial transition of resource-exhausted cities is the focus of attention, and air quality has naturally become an important indicator to measure the sustainable development quality. Aerosol optical depth (AOD) is an important parameter to indicate air quality. This paper aimed to study the influence of industrial transition on air quality and provide a list of recommendations and management strategies for sustainable development in resource-exhausted cities. Results showed the secondary industry played important roles for economic development before 2015, however, it decreased after 2014, and the tertiary industry played more and more important roles from 2015. Analyses of the spatial distribution of AOD in each year showed that AOD was relatively higher in urban areas with concentrated population, and the threshold range of AOD value with high area ratio in spatial distribution decreased gradually, which was consistent with the analysis results of time series. Results of correlation analyses indicated that air temperature and land surface temperature were the main natural meteorological factors influencing AOD. Gross population, SO2 emission and the cultivated land area were the main socio-economic factors influencing AOD. It could be concluded that the industrial transition of the city has achieved good results, the economic structure has been gradually optimized and adjusted, and the air quality has gradually improved over industrial sustainability transition. Scientific exploitation, energy conservation, application of clean energy and industrial structure optimization would be effective measures for sustainable development.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India.

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

[Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area].

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 µg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 µg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 µg·m-3 to 112.780 µg·m-3 and the lowest values ranging from 5.000 µg·m-3 to 7.899 µg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 µg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

Spatial-Temporal resolution implementation of cloud-aerosols data through satellite cross-correlation.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard Terra and Aqua satellites provides measurements of several atmospheric parameters. This paper focuses on the cloud fraction data representing the number of cloudy pixels divided by the total number of pixels, and available through 1° x 1° grids spatial resolution with daily or monthly temporal resolution. The aim of the study is to propose a novel method called The Spatial-Temporal Implementation Algorithm (STIA) for analysing satellite daily 1° x 1°grid cloud fraction average values for•Comparing two datasets retrieved by MODIS aboard Aqua and Terra satellites to obtain information on the cloud formation in the afternoon and morning, respectively, thus enhancing the temporal resolution.•Comparing the actual parameter with others retrieved by instruments aboard of different satellites characterized by a better resolution. As an example of STIA application, this study uses the Aerosol Optical Depth (AOD) collected by the Ozone Monitoring Instrument (OMI) on board of Aura satellite for comparison with MODIS instrument to achieve and enhanced spatial resolution of the grid-cell.

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.

[Spatial and Temporal Variations in PM1 Concentration and its Correlation with AOD in China from 2014 to 2017].

Based on the PM1 mass concentration data from all the air quality monitoring stations in China from 2014 to 2017, the temporal and spatial distribution characteristics of PM1 concentration were studied using the time series statistical and spatial hierarchical clustering methods, and the PM1 spatiotemporal evolution characteristics were revealed. Combined with AOD data of the MODIS remote-sensing satellite, the temporal and spatial variation in PM1-AOD correlation was analyzed on a fine scale. The results showed that, from 2014 to 2017, the annual average PM1 concentration in China decreased yearly, the seasonal PM1 concentration showed the characteristics of "high in winter and low in summer," and the monthly average PM1 concentration showed a "U"-shaped variation. An "M"-shaped PM1 variation pattern was presented before and after the holidays. Weekly variation showed that high PM1 values occurred on Mondays and Fridays, and low ones occurred on Sundays. Based on the spatial clustering method, the national average annual PM1 concentration in China was divided into seven categories, and the overall spatial distribution pattern was "high in the east and low in the west and high in the north and low in the south." The highest and the lowest values of average PM1 concentration occurred in central China(54.59 µg·m-3) and in Xinjiang-Qinghai-Xizang(11.37 µg·m-3), respectively. The PM1-AOD relationship was positively correlated as a whole, the highest correlation coefficient was 0.55 in central China, and the lowest value was 0.36 in central and southern China.

Bacteria Associated with Acute Oak Decline: Where Did They Come From? We Know Where They Go.

Acute oak decline is a high-impact disease causing necrotic lesions on the trunk, crown thinning and the eventual death of oak. Four bacterial species are associated with the lesions-Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and Lonsdalea Britannica-although an epi-/endophytic lifestyle has also been suggested for these bacteria. However, little is known about their environmental reservoirs or their pathway to endophytic colonisation. This work aimed to investigate the ability of the four AOD-associated bacterial species to survive for prolonged periods within rhizosphere soil, leaves and acorns in vitro, and to design an appropriate method for their recovery. This method was trialled on field samples related to healthy and symptomatic oaks. The in vitro study showed that the majority of these species could survive for at least six weeks within each sample type. Results from the field samples demonstrated that R. victoriana and G. quercinecans appear environmentally widespread, indicating multiple routes of endophytic colonisation might be plausible. B. goodwinii and L. britannica were only identified from acorns from healthy and symptomatic trees, indicating they may be inherited members of the endophytic seed microbiome and, despite their ability to survive outside of the host, their environmental occurrence is limited. Future research should focus on preventative measures targeting the abiotic factors of AOD, how endophytic bacteria shift to a pathogenic cycle and the identification of resilient seed stock that is less susceptible to AOD.