Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM${}_{10}$ concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM${}_{10}$ are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM${}_{10}$ was the HCH station. The highest PM${}_{10}$ concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μ$\mathsf{\mu }$g/m${}^3$. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and a statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio-temporal variations of PM${}_{10}$ concentrations. Furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM${}_{10}$ levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district. Full article

Rocks ejected from a volcanic eruption often cause loss of lives and structures. Aerodynamic characteristics are needed for evaluating motions of volcanic rocks for the reduction of damage. Falling motions of volcanic rock were measured by using models imitated the configuration of cinders collected at the site of the experiment, Sakurajima volcano. Two types, one with sharp edges and one without sharp edges, were selected as representative of cinder and a sphere was selected as reference model. The falling motions of the models dropped down from a drone were recorded by video camera and a stand-alone measuring system that included a pressure sensor, acceleration and angular velocity sensors in the models. The motion, posture, velocity and acceleration of the model were obtained in order to measure the three-dimensional falling trajectory. The drag and the deviation angle between relative wind direction and wind force direction were examined. The variation of the drag coefficient and the deviation angle with Reynolds number was clarified. Full article

The PM${}_{2.5}$ concentration model is the key to predict PM${}_{2.5}$ concentration. During the prediction of atmospheric PM${}_{2.5}$ concentration based on prediction model, the prediction model of PM${}_{2.5}$ concentration cannot be usually accurately described. For the PM${}_{2.5}$ concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM${}_{2.5}$ concentration may be different, and the single model cannot play the corresponding ability to predict PM${}_{2.5}$ concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM${}_{2.5}$ concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM${}_{2.5}$ concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM${}_{2.5}$ concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM${}_{2.5}$ concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM${}_{2.5}$ concentration prediction is improved in whole time period. Full article

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. Full article

A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water. Full article

At present, high-resolution drought indices are scarce, and this problem has restricted the development of refined drought analysis to some extent. This study explored the possibility of calculating the standardized precipitation index (SPI) with short-term precipitation sequences in China, based on data from 2416 precipitation observation stations covering the time period from 1961 to 2019. The result shows that it is feasible for short-sequence stations to calculate SPI index, based on the spatial interpolation of the precipitation distribution parameters of the long-sequence station. Error analysis denoted that the SPI error was small in east China and large in west China, and the SPI was more accurate when the observation stations were denser. The SPI error of short-sequence sites was mostly less than 0.2 in most areas of eastern China and the consistency rate for the drought categories was larger than 80%, which was lower than the error using the 30-year precipitation samples. Further analysis showed that the estimation error of the distribution parameters $\beta$ and $q$ was the most important cause of SPI error. Two drought monitoring examples show that the SPI of more than 50,000 short-sequence sites can correctly express the spatial distribution of dry and wet and have refined spatial structure characteristics. Full article

The ionospheric weather is affected not only from above by the Sun but also from below by processes in the lower-lying atmospheric layers. One of the most pronounced atmospheric phenomena is the sudden stratospheric warming (SSW). Three major SSW events from the periods of very low solar activity during January 2009, February 2018, and December 2018/January 2019 were studied to evaluate this effect of the neutral atmosphere on the thermosphere and the ionosphere. The main question is to what extent the ionosphere responds to the SSW events with focus on middle latitudes over Europe. The source of the ionospheric data was ground-based measurements by Digisondes, and the total electron content (TEC). In all three events, the ionospheric response was demonstrated as an increase in electron density around the peak height of the F2 region, in TEC, and presence of wave activity. We presume that neutral atmosphere forcing and geomagnetic activity contributed differently in individual events. The ionospheric response during SSW 2009 was predominantly influenced by the neutral lower atmosphere. The ionospheric changes observed during 2018 and 2018/2019 SSWs are a combination of both geomagnetic and SSW forcing. The ionospheric response to geomagnetic forcing was noticeably lower during time intervals outside of SSWs. Full article

In the present study, we analyzed the particle size distribution (PSD) of falling volcanic ash particles measured using optical disdrometers during six explosive eruptions of the Sakurajima volcano in Kagoshima Prefecture, Japan. Assuming the gamma PSD model, which is commonly used in radar meteorology, we examined the relationships between each of the gamma PSD parameters (the intercept parameter, the slope parameter, and the shape parameter) calculated by the complete moment method. It was shown that there were good correlations between each of the gamma PSD parameters, which might be one of the characteristics of falling volcanic ash particles. We found from the normalized gamma PSD analysis that the normalized intercept parameter and mass-weighted mean diameter are suitable for estimating the ash fall rate. We also derived empirical power law relationships between pairs of integrated PSD parameters: the ash fall rate, the volcanic ash mass concentration, the reflectivity factor, and the total number of ash particles per unit volume. The results of the present study provide essential information for studying microphysical processes in volcanic ash clouds, developing a method for quantitative ash fall estimation using weather radar, and improving ash transport and sedimentation models. Full article

This paper showed different plume behaviors of exhausts from different number of stacks of the container ship, using CFD code PHOENICS version 6.0. The plume behavior was quantitatively analyzed by mass fraction of the pollutant in the exhaust and plume heights. Three simplified typical configurations were constructed by CFD according to the investigation of container ships. The configurations included a single main stack (BL1), one main stack and multiple auxiliary stacks (BL2), and two main stacks and multiple auxiliary stacks (BL3). All the main stacks had the same emission characteristics, and all the auxiliary stacks had the same emission characteristics. The results show that the transmission and diffusion characteristics of the exhaust from multiple stacks are different from those of the exhaust from a single stack. In BL2 and BL3 simulations, the maximum mass fraction of SO₂ in the exhaust (C1_max) of multiple stack emissions was approximately 329% and 269% higher than that of single stack emissions over the main stack, respectively, and the plume height of multiple stack emissions is higher than that of single stack emissions. In BL2 and BL3 simulations, the plume height of multiple stack emissions was 41% and 75% higher than that of single stack emissions, respectively. The increase of C1_max, due to multiple stack emissions, is weakened as the distance of the stacks increase. The difference in plume behavior between multiple stack emissions and single stack emissions is of great significance for air quality management and pollution control in port areas. Full article

Vegetation plays a key role in partitioning energy at the surface. Meteorological and Climate Models, both global and regional, implement vegetation using two parameters, the vegetation fraction and the leaf area index, obtained from satellite data. In most cases, models use average values for a given period. However, the vegetation is subject to strong inter-annual variability. In this work, the sensitivity of the near surface air temperature to changes in the vegetation is analyzed using a regional climate model (RCM) over the Iberian Peninsula. The experiments have been designed in a way that facilitates the physical interpretation of the results. Results show that the temperature sensitivity to vegetation depends on the time of year and the time of day. Minimum temperatures are always lower when vegetation is increased; this is due to the lower availability of heat in the ground due to the reduction of thermal conductivity. Regarding maximum temperatures, the role of increasing vegetation depends on the available moisture in the soil. In the case of hydric stress, the maximum temperatures increase, and otherwise decrease. In general, increasing vegetation will lead to a higher daily temperature range, since the decrease in minimum temperature is always greater than the decrease for maximum temperature. These results show the importance of having a good estimate of the vegetation parameters as well as the implications that vegetation changes due to natural or anthropogenic causes might have in regional climate for present and climate change projections. Full article

Non-road equipment has been an important source of pollutants that negatively affect air quality in China. An accurate emission inventory for non-road equipment is therefore required to improve air quality. The objective of this paper was to characterize emissions from typical diesel-fueled material handling equipment (loaders and cranes) using a portable emission measurement system. Instantaneous, modal, and composite emissions were quantified in this study. Three duty modes (idling, moving, and working) were used. Composite emission factors were estimated using modal emissions and time-fractions for typical duty cycles. Results showed that emissions from loaders and cranes were higher and more variable for the moving and working modes than the idling mode. The estimated fuel-based CO, HC, NO, and PM_2.5 composite emission factors were 21.7, 2.7, 38.2, and 3.6 g/(kg-fuel), respectively, for loaders, and 8.7, 2.4, 28.3, and 0.3 g/(kg-fuel), respectively, for cranes. NO emissions were highest and should be the main focus for emission controls. CO, HC, NO, and PM_2.5 emissions measured were different from emission factors in the US Environmental Protection Agency NONROAD model and the Chinese National Guideline for Emission Inventory Development for Non-Road Equipment. This indicates that improving emission inventory accuracy for non-road equipment requires more real-world emission measurements. Full article

Winter persistent extreme cold events (WPECEs) often cause great damage to the development of economies and people’s lives. The sub-seasonal variation of the atmospheric circulation is regarded as one of important causes of extreme weather, and is key to propel the extended period prediction. In this paper, we mainly analyze the WPECEs in Xinjiang region and their relationship with the sub-seasonal variation of the East Asian polar front jet (PFJ). The results suggest the persistent extreme cold event (equal or greater than 7 days) occurs most frequently in Xinhe County of Xinjiang region, with obvious inter-annual and inter-decadal variations. Further analysis shows that the variation of the mean temperature in the key area has characteristics of intra-seasonal variation when the WPECE occurs. The result of composite analysis shows that this intra-seasonal variation is related to the sub-seasonal variation of atmospheric circulation, especially the PFJ anomalous activity near Lake Balkhash. By using the power spectrum analysis method, note that the PFJ activity has the characteristics of quasi-biweekly oscillation (QBWO) in WPECEs. On quasi-biweekly scale (10–20-day filtered), the weakening of PFJ, the intensification of the zonal easterly wind in the upper troposphere, the accumulation of the strong cold air, and the intensification of the meridional northerly wind in the lower troposphere enhance the occurrence of WPECEs in Xinjiang. Further investigation indicates that the quasi-biweekly PFJ mainly propagates eastward and southward before the WPECE occurs in Xinjiang, China. Full article

Vinyl chloride (VC) is classified as a group 1 carcinogen to humans by the International Agency for Research on Cancer, and inhalation is considered to be an important route of occupational exposure. In addition, increasing numbers of studies have observed adverse health effects in people living in the vicinity of petrochemical complexes. The objective of this study was to investigate the adverse in vivo health effects on the lungs and liver caused by pulmonary exposure to low-level VC. BALB/c mice were repeatedly intranasally administrated 50 µL/mouse VC at 0, 1, and 200 ng/mL (5 days/week) for 1, 2, and 3 weeks. We observed that exposure to 1 and 200 ng/mL VC significantly increased the tidal volume (μL). Dynamic compliance (mL/cmH₂O) significantly decreased after exposure to 200 ng/mL VC for 3 weeks. Total protein, lactate dehydrogenase (LDH), and interleukin (IL)-6 levels in bronchoalveolar lavage fluid (BALF) significantly increased after exposure to 200 ng/mL VC for 2 and/or 3 weeks. Significant decreases in 8-isoprostane and caspase-3 and an increase in IL-6 in the lungs were found after VC exposure for 2 and/or 3 weeks. We observed that aspartate aminotransferase (AST), alkaline phosphatase (ALKP), albumin (ALB), and globulin (GLOB) had significantly increased after three weeks of VC exposure, whereas the ALB/GLOB ratio had significantly decreased after 3 weeks of exposure to VC. IL-6 in the liver increased after exposure to 1 ng/mL VC, but decreased after exposure to 200 ng/mL. IL-1β in the liver significantly decreased following exposure to 200 ng/mL VC, whereas tumor necrosis factor (TNF)-α and caspase-3 significantly increased. Hepatic inflammatory infiltration was confirmed by histological observations. In conclusion, sub-chronic and repeated exposure to low levels of VC can cause lung and liver toxicity in vivo. Attention should be paid to all situations where humans are frequently exposed to elevated VC levels such as workplaces or residents living in the vicinity of petrochemical complexes. Full article

The increasing availability of low-cost air quality sensors has led to novel sensing approaches. Distributed networks of low-cost sensors, together with data fusion and analytics, have enabled unprecedented, spatiotemporal resolution when observing the urban atmosphere. Several projects have demonstrated the potential of different approaches for high-resolution measurement networks ranging from static, low-cost sensor networks over vehicular and airborne sensing to crowdsourced measurements as well as ranging from a research-based operation to citizen science. Yet, sustaining the operation of such low-cost air quality sensor networks remains challenging because of the lack of regulatory support and the lack of an organizational framework linking these measurements to the official air quality network. This paper discusses the logical inclusion of lower-cost air quality sensors into the existing air quality network via a dynamic field calibration process, the resulting sustainable business models, and how this expansion can be self-funded. Full article