Black carbon transport from the Santiago Metropolitan Area, Chile, up to the adjacent Andes Cordillera and its glaciers is of major concern. Its deposition accelerates the melting of the snowpack, which could lead to stress on water supply in addition to climate feedback. A proposed pathway for this transport is the channelling through the network of canyons that connect the urban basin to the elevated summits, as suggested by modelling studies, although no observations have validated this hypothesis so far. In this work, atmospheric measurements from a dedicated field campaign conducted in winter 2015, under severe urban pollution conditions, in Santiago and the Maipo canyon, southeast of Santiago, are analysed. Wind (speed and direction) and particulate matter concentrations measured at the surface and along vertical profiles, demonstrate intrusions of thick layers (up to 600 m above ground) of urban black carbon deep into the canyon on several occasions. Transport of PM down-valley occurs mostly through shallow layers at the surface except in connection with deep valley intrusions, when a secondary layer in altitude with return flow (down-valley) at night is observed. The transported particulate matter is mostly from the vicinity of the entrance to the canyon and uncorrelated to concentrations observed in downtown Santiago. Reanalyses data show that for 10% of the wintertime days, deep intrusions into the Maipo canyon are prevented by easterly winds advecting air pollutants away from the Andes. Also, in 23% of the cases, intrusions proceed towards a secondary north-eastward branch of the Maipo canyon, leaving 67% of the cases with favourable conditions for deep penetrations into the main Maipo canyon. Reanalyses show that the wind directions associated to the 33% anomalous cases are related to thick cloud cover and/or the development of coastal lows.

In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM2.5). This study aimed to construct the AOD-PM2.5 estimation models over Taiwan. The AOD-PM2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R2 based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0 μg/m3 (34%) and 12.9 μg/m3 (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM2.5 estimations at locations without surface stations. The estimation revealed PM2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM2.5 concentrations over Taiwan consistently declined during 2005–2015. The AOD-PM2.5 model is a reliable and validated method for estimating PM2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

This study assessed the relationship between the satellite Aerosol Optical Depths (AODs) and the ground monitored concentrations of particulate matter (PM) mass and its major constituents (black carbon–BC, organic carbon–OC, sulfates and nitrates), respectively. Both component AOD and total AOD products of Multi–angel Imaging Spectro Radiometer (MISR) were used for comparison along with the AOD product of the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground PM data available during the period from 2004 to 2010 at the Asian Institute of Technology (AIT), a suburb site of the Bangkok Metropolitan Region, was used. MODIS and MISR AODs were validated against Sun photometer AOD, monitored at the Pimai AERONET station which showed strong linear regression with high R2 values of 0.87 and 0.92, respectively. The correlation coefficients between MODIS and MISR AODs and PM mass concentrations, respectively, were improved after exclusion of observations with cloud cover above 3/10. The R values (square root of determination coefficient R2) for linear relationships between PM10 and MODIS AOD were accordingly increased from 0.33 to 0.58 for MODIS AOD and from 0.25 to 0.54 for MISR AOD, while those for PM2.5 were improved from 0.30 to 0.55 for MODIS AOD and from 0.31 to 0.43 for MISR AOD. The stepwise regression was conducted to analyze the relationship between MISR component AODs and the mass concentration of PM10 and PM2.5, respectively, as well as their constituents. Higher R values were obtained for all regression equations using MISR component AODs as compared to those using total AOD. MISR component AODs showed higher capacity for monitoring daily BC (R=0.74–0.75) and sulfates (R=0.72), as compared to nitrates (R=0.52–0.54) and hourly OC (R=0.47). The potential of MISR component AODs for ambient PM monitoring should be explored and applied in other regions.

Ambient air quality assessment and management plays an important role in the current European Union policy. Among others, air pollution by PM10 is being monitored. A previous analysis of PM10 aimed at identifying factors affecting air pollution in stations in the City of Brno using available observed meteorological variables. However, the studied model cannot be used for predicting the level of PM10 pollution because the included meteorological variables are not available exactly at the time when the prediction is requested. In that case, we should base the predictions on available predicted variables, namely on temperature, wind direction, wind speed, and cloud cover. A comparison of obtained predictions with the observed values of PM10 during a testing period allows us to evaluate a loss of prediction quality when the predicted covariates are used instead of the observed ones. The presented analysis based on test of symmetry and test of homogeneity of the marginal distribution of two–way contingency tables shows that the loss of prediction quality by employing the predicted meteorological variables is non–significant in the studied case. This observation suggests that the model with predicted meteorological variables can be employed in environmental management processes.

Nitrogen deposition from air pollution is increasingly reaching alpine lakes where the addition of nitrate and ammonium to sensitive surface waters can cause acidification and/or eutrophication. Thirty years of sampling in the Wind River Range, WY, has shown some lakes increasing in nitrogen. We sought to (1) determine if nutrient concentrations in Deep Lake increase during snowmelt when atmospheric deposition is released from the snowpack and (2) assess if the sampling season, location, meteorological factors, and time of day samples are collected influence lake chemistry metrics, to inform monitoring. We analyzed water samples from the outlet of Deep Lake in peak snowmelt (June) and from the inlet, outlet, and middle of Deep Lake when the basin was snow free (August). In June, outlet samples were more acidic, and nitrogen content was three times August levels. Acid neutralizing capacity (ANC) declined with snowmelt. August inlet samples were higher in nutrients than outlet and mid-lake samples. Our results indicate that atmospheric pollution in the snowpack enters the lake with snowmelt. Although Deep Lake has not acidified, ANC levels indicate a risk of episodic acidification if nitrogen deposition continues to increase. When monitoring lakes at risk for episodic acidification, sampling during the late snowmelt pulse should be prioritized. Simplified sampling protocols may be used in some lakes, as epilimnion and outlet samples were nearly identical. The time of day and cloud cover did not affect lake chemistry, while wind speed and precipitation weakly increased August ANC and June pH, respectively.

Global solar radiation is the total amount of solar energy received on a horizontal surface and defined as the sum of direct, diffused, and reflected solar radiation. Global solar radiation is an important variable in agricultural, meteorological, hydrological, and climatological studies. The purpose of this paper is to develop an effective method to estimate the daily global solar radiation using different atmospheric properties detected from satellite data, including cloud fraction, cloud optical depth, aerosol optical depth, aerosol exponent, aerosol index, and precipitable water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) and ozone monitoring instrument (OMI) daytime data in the urban area of Mashhad, Iran, during the years from 2000 to 2018. Based on seven combinations of the atmospheric properties, models were developed using a standard statistical method, namely, multiple linear regression method and a specific class of artificial neural networks, namely, feedforward multilayer perceptron. The efficiency of the models was compared for the assessment of the daily global solar radiation based on the combinations of the input data. For both methods, 80% percent of the data are used for model development and the remaining data for validation. Results of pairwise statistics indicate that, on average, the estimates were more accurate using the artificial neural networks than the regression method. Results show that in both methods, the accuracy of estimation improves when cloud fraction is used as a predictor. This implies the significant effect of cloud cover on solar radiation. However, using the cloud optical depth decreases the accuracy of the estimation of global solar radiation, i.e., the least accurate model is the one with cloud fraction and cloud optical depth for the neural network method and the model with CF and AE for the regression method. The estimation error comes from the inaccuracy in measuring cloud optical depth that depends on satellite sensor resolution and the inhomogeneity of types and microphysical properties of clouds over the study area. Due to the arid climate of the study area, the precipitable water vapor content does not considerably affect radiation attenuation. The best estimate is earned by cloud fraction and aerosol index as inputs indicating the simultaneous role of aerosol and cloud in global solar radiation. Aerosol index considers the effect of absorbing aerosols such as black carbon and dust and is a complementary information to the cloud cover. The results imply that both methods have the potential to achieve an operational stage, taking advantage of the better availability of satellite data. Even though the artificial neural network is found to be more accurate than multiple linear regression, using the regression method is recommended because it is more easy to use. Results show that the effective variables vary in different seasons. In both methods, estimation error is highest in the spring and lowest in the fall and winter. The high inaccuracy may be due to the high sensitivity of radiative transfer to atmospheric condition in spring. On the other hand, the high accuracy may be caused by the less solar radiation fluctuations during fall and winter because of the lower solar radiation flux.

We aimed to understand relationships of the weather as biometeorological and hospital admissions due to other forms of heart disease by subtypes, which have been paid less attention, in a national setting in recent years. This is an ecological study. Ten percent of daily hospital admissions of the included hospitals (n = 1618) across Germany that were available between 1 January 2009 and 31 December 2011 (n = 5,235,600) were extracted from Statistisches Bundesamt, Germany. We identified I30–I51 other forms of heart disease by the International Classification of Diseases version 10 as the study outcomes. Daily weather data from 64 weather stations that have covered 13 German states, including air temperature, humidity, wind speed, cloud cover, radiation flux and vapour pressure, were obtained and generated into physiologically equivalent temperature (PET). Admissions due to other diseases of pericardium, nonrheumatic mitral valve disorders, nonrheumatic aortic valve disorders, cardiomyopathy, atrioventricular and left bundle-branch block, other conduction disorders, atrial fibrillation and flutter, and other cardiac arrhythmias peaked when PET was between 0 and 10 °C. Complications and ill-defined descriptions of heart disease admissions peaked at PET 0 °C. Cardiac arrest and heart failure admissions peaked when PET was between 0 and −10 °C while the rest did not vary significantly. A common drop of admissions was found when PET was above 10 °C. More medical resources could have been needed for heart health on days when PETs were <10 °C than on other days. Adaptation to such weather change for medical professionals and the general public would seem to be imperative.

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. Maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition, the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.

We aimed to understand relationships of the weather as biometeorological and hospital admissions due to diseases of arteries and veins by subtypes, which have been scarcely studied, in a national setting in recent years. This is an ecological study. Ten percent of daily hospital admissions from the included hospitals (n = 1,618) across Germany that were available between 1 January 2009 and 31 December 2011 (n = 5,235,600) were extracted from Statistisches Bundesamt, Germany. We identified I70-I79 Diseases of arteries, arterioles and capillaries and I80-I89 Diseases of veins, lymphatic vessels and lymph nodes by International Classification of Diseases version 10 as the study outcomes. Daily weather data from 64 weather stations that covered 13 German states including air temperature, humidity, wind speed, cloud cover, radiation flux and vapour pressure were obtained and generated into physiologically equivalent temperature (PET). Two-way fractional-polynomial prediction was plotted with 95 % confidence intervals. For most of the subtypes from diseases of arteries and veins, hospital admissions slightly peaked in spring and dropped when PET was at 10 °C. There were no other large differences across 12 months. Admissions of peripheral vascular diseases, arterial embolism and thrombosis, phlebitis and thrombophlebitis, oesophageal varices and nonspecific lymphadenitis peaked when PET was between 0 and −10 °C, while others peaked when PET was between 0 and 10 °C. More medical resources could have been needed on days when PETs were at −10 to 10 °C than on other days. Adaptation to such weather change for health professionals and the general public would seem to be imperative.