ALBERT

Description: The Southwestern USA has been identified as one of the most persistent dust-producing regions of North America, where exposure to inhalable particulate matter (PM10) originating from desertic landscape during dust events/dust exposures (DEs) can reach hazardous levels. El Paso, Texas’s ambient air has reached hazardous levels of PM10 from dust with near zero visibility due to these natural events originating in the surrounding Chihuahuan Desert. The aim of this study was to investigate whether dust exposures in El Paso (generally acute, short-term exposures from nearby source areas) are associated with significant increases in hospitalizations on the day of the exposure and up to seven days afterwards. Using a Poisson regression, it was found that the relative risks of hospitalizations due to a variety of conditions were associated with dust exposures (through increases of 100 μg/m3 maximum hourly PM10 and/or increases of 4.5 m/s maximum hourly wind speed) in El Paso County, Texas between 2010 and 2014. Valley fever, coronary atherosclerosis, genitourinary diseases, neurodegenerative diseases, injury and poisoning, circulatory system conditions, respiratory system diseases, births, septicemia, Associated Diseases (the aggregation of hospital admissions for all causes, each associated with at least 5% of hospitalizations), and all ICD-9 admissions were significantly positively associated with dust exposures, indicated from higher to lower significant risk, at different lag periods after exposure. These findings, showing that an association does exist between dust exposures and hospitalizations, have important implications for residents of the world’s dryland cities.

Description: The increase of the vertical scaling exponent of the horizontal wind Hv(s) with altitude from the surface of the Pacific Ocean to 13 km altitude, as observed by GPS dropsondes, is investigated. An explanation is offered in terms of the decrease of gravitational force and decrease of quenching efficiency of excited photofragments from ozone photodissociation with increasing altitude (decreasing pressure). Turbulent scaling is examined in both the vertical from dropsondes and horizontal from aircraft observations; the scaling exponents H for both wind speed and temperature in both coordinates are positively correlated with traditional measures of jet stream strength. Interpretation of the results indicates that persistence of molecular velocity after collision induces symmetry breaking emergence of hydrodynamic flow via the mechanism first modelled by Alder and Wainwright, enabled by the Gibbs free energy carried by the highest speed molecules. It is suggested that the combined effects have the potential to address the cold bias in numerical models of the global atmosphere.

Description: Aerosol columnar size distributions (SDs) are commonly provided by aerosol inversions based on measurements of both spectral extinction and sky radiance. These inversions developed for a fully clear sky offer few SDs for areas with abundant clouds. Here, we estimate SDs from spectral extinction data alone for cloudy coastal and maritime regions using aerosol refractive index (RI) obtained from chemical composition data. Our estimation involves finding volume and mean radius of lognormally distributed modes of an assumed bimodal size distribution through fitting of the spectral extinction data. We demonstrate that vertically integrated SDs obtained from aircraft measurements over a coastal site have distinct seasonal changes, and these changes are captured reasonably well by the estimated columnar SDs. We also demonstrate that similar seasonal changes occur at a maritime site, and columnar SDs retrieved from the combined extinction and sky radiance measurements are approximated quite well by their extinction only counterparts (correlation exceeds 0.9) during a 7-year period (2013–2019). The level of agreement between the estimated and retrieved SDs depends weakly on wavelength selection within a given spectral interval (roughly 0.4–1 µm). Since the extinction-based estimations can be performed frequently for partly cloudy skies, the number of periods where SDs can be found is greatly increased.

Description: The macroscopic characteristics of clouds over Lanzhou, China were investigated using micro-pulse lidar data from September 2005 to November 2011. The results show that the mean of the cloud base height, cloud peak height, cloud top height and cloud thickness during the observation was 4.03 km, 4.81 km, 5.50 km and 1.47 km, respectively; the maximum frequency of the cloud base height, cloud peak height, cloud top height and cloud thickness was 25.7% in the range of 1–2 km, 16.2% in the range of 2–3 km, 14.6% in the range of 2–3 km and 42.2% in the range of 1–2 km, respectively; the maximum frequency of cloud base height was 24.2%, 24.6%, 29.7% and 21.4% in spring, summer, autumn and winter, respectively, all in the range of 1–2 km, and middle clouds occurred most frequently at 41.4%, followed by low clouds (33.7%) and high clouds (24.9%) during the observation period; the maximum frequency of cloud peak height was 15.8% in the range of 3–4 km, 18% in the range of 4–5 km, 20% in the range of 2–3 km in autumn and 18.6% in the range of 5–6 km in winter; the maximum frequency of cloud top height was 14% in the range of 3–4 km in spring, 16% in the range of 4–5 km in summer, 20.1% in the range of 2–3 km in autumn and 17.8% in the range of 7–8 km in winter; the maximum frequency of cloud thickness was 44.9%, 35.6% and 52% in the range of 1–2 km in spring, summer and winter, respectively, while it was 44.9% in the range of 0–1 km in autumn; the cloud thickness was mostly less than 3 km; generally, the thicker of cloud, the less the frequency.

Description: As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.

Description: Understanding transportation and deposition (TD) of aerosol particles in the human respiratory system can help clinical treatment of lung diseases using medicines. The lung airway diameters and the breathing capacity of human lungs normally increase with age until the age of 30. Many studies have analyzed the particle TD in the human lung airways. However, the knowledge of the nanoparticle TD in airways of infants and children with varying inhalation flow rates is still limited in the literature. This study investigates nanoparticle (5 nm ≤ dp ≤ 500 nm) TD in the lungs of infants, children, and adults. The inhalation air flow rates corresponding to three ages are considered as Qin=3.22 L/min (infant), 8.09 L/min (Child), and Qin=14 L/min (adult). It is found that less particles are deposited in upper lung airways (G0–G3) than in lower airways (G12–G15) in the lungs of all the three age groups. The results suggest that the particle deposition efficiency in lung airways increases with the decrease of particle size due to the Brownian diffusion mechanism. About 3% of 500 nm particles are deposited in airways G12–G15 for the three age groups. As the particle size is decreased to 5 nm, the deposition rate in G12–G15 is increased to over 95%. The present findings can help medical therapy by individually simulating the distribution of drug-aerosol for the patient-specific lung.

Description: Emissions from residential solid fuel burning in densely populated low-income settlements is a significant source of air pollution over the South African Highveld. The area is densely populated and highly industrialized, resulting in high concentrations of pollutants over the area. Although emissions from industrial sources are much larger, exposure to household emissions poses the most significant risk to human health. Interventions aimed at reducing solid fuel burning in low-income settlements on the Highveld have the potential to reduce exposure, but quantifying their true impact remains a challenge. We aimed to estimate the health and associated economic benefits of the regional implementation of thermal insulation as intervention measure in low-income settlements to predict the potential impact on the regional scale. We used a land use type regression model to estimate pre-intervention PM2.5 concentrations over the Highveld and then used sampled post-intervention air quality data from a pilot offset programme to relate changes in air quality to changes in avoided premature mortalities. We estimate that the large-scale implementation of this intervention could result in 143 avoided premature mortalities with an estimated economic benefit of just under ZAR (2011) 341.6 million, equivalent to USD (2011) 49.4 million.

Description: Using data collected by the GNSS dual-frequency receivers network, de-trended TEC maps were generated to identify and characterize the medium-scale traveling ionospheric disturbances (MSTIDs) over the South American equatorial region (latitude: 0∘ to 15∘ S and longitude: 30∘ to 55∘ W) during solar cycle 24 (from January 2014 to December 2019). A total of 712 MSTIDs were observed during quiet geomagnetic conditions. The Frequency of occurrence of MSTID is high during the solar maximum and low in the minimum phase. This might be due to the solar cycle dependence of gravity wave activity in the lower atmosphere and gravity wave propagation conditions in the thermosphere. The predominant daytime MSTIDs, representing 80% of the total observations, occurred in winter (June-August season in the southern hemisphere) with the secondary peak in the equinox; while the evening time MSTIDs, representing 18% of the entire events, occurred in summer (December to February season) and equinox (March to May and September to November), and the remaining 2% of the MSTIDs were observed during nighttime. The seasonal variation of the MSTID events was attributed to the source mechanisms generating them, the wind filtering and dissipation effects, and the local time dependency. The horizontal wavelengths of the MSTIDs were mostly concentrated between 500 and 800 km, with the mean value of 667 ± 131 km. The observed periods ranged from 30 to 45 min with the mean value of 36 ± 7 min. The observed horizontal phase speeds were distributed around 200 to 400 m/s, with the corresponding mean of 301 ± 75 m/s. The MSTIDs in the winter solstice and equinoctial months preferentially propagated northeastward and northwestward. Meanwhile, during the summer solstice, they propagated in all directions. The anisotropy of the propagation direction might be due to several reasons: the wind and dissipative filtering effects, ion drag effects, the primary source region, and the presence of the secondary or tertiary gravity waves in the thermosphere. Atmospheric gravity waves from strong convective sources might be the primary precursor for the observed equatorial MSTIDs. In all seasons, we noted that the MSTIDs propagating southeastward were probably excited by the likely gravity waves generated by the intertropical convergence zone (ITCZ).

Description: The major organic compositions from biomass burning emissions are monosaccharide derivatives from the breakdown of cellulose, generally accompanied by small amounts of straight-chain, aliphatic, oxygenated compounds, and terpenoids from vegetation waxes, resins/gums, and other biopolymers. Levoglucosan from cellulose can be utilized as a specific or general indicator for biomass combustion emissions in aerosol samples. There are other important compounds, such as dehydroabietic acid, syringaldehyde, syringic acid, vanillic acid, vanillin, homovanillic acid, 4-hydroxybenzoic acid, and p-coumaric acid, which are additional key indicators of biomass burning. In this review, we will address these tracers from different types of biomass burning and the methods used to identify the sources in ambient aerosols. First, the methods of inferring biomass burning types by the ratio method are summarized, including levoglucosan/mannose, syringic acid/vanillic acid, levolgucosan/K+, vanillic acid/4-hydroxybenzoic acid, levoglucosan/OC, and levoglucosan/EC to infer the sources of biomass burning, such as crop residual burning, wheat burning, leaf burning, peatland fire, and forest fire in Asia. Second, we present the source tracer ratio methods that determine the biomass combustion types and their contributions. Finally, we introduce the PCA (Principal component analysis) and PMF (Positive matrix factor) methods to identify the type of biomass burning and its contributions according to emission factors of different species in various plants such as softwood, hardwood, and grass.

Description: The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.