Factors influencing environmental sampling recovery of healthcare pathogens from non-porous surfaces with cellulose sponges

Results from sampling healthcare surfaces for pathogens are difficult to interpret without understanding the factors that influence pathogen detection. We investigated the recovery of four healthcare-associated pathogens from three common surface materials, and how a body fluid simulant (artificial test soil, ATS), deposition method, and contamination levels influence the percent of organisms recovered (%R). Known quantities of carbapenemase-producing KPC+ Klebsiella pneumoniae (KPC), Acinetobacter baumannii, vancomycin-resistant Enterococcus faecalis, and Clostridioides difficile spores (CD) were suspended in Butterfield’s buffer or ATS, deposited on 323cm2 steel, plastic, and laminate surfaces, allowed to dry 1h, then sampled with a cellulose sponge wipe. Bacteria were eluted, cultured, CFU counted and %R determined relative to the inoculum. The %R varied by organism, from <1% (KPC) to almost 60% (CD) and was more dependent upon the organism’s characteristics and presence of ATS than on surface type. KPC persistence as determined by culture also declined by >1 log10 within the 60 min drying time. For all organisms, the %R was significantly greater if suspended in ATS than if suspended in Butterfield’s buffer (p<0.05), and for most organisms the %R was not significantly different when sampled from any of the three surfaces. Organisms deposited in multiple droplets were recovered at equal or higher %R than if spread evenly on the surface. This work assists in interpreting data collected while investigating a healthcare infection outbreak or while conducting infection intervention studies.

Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard Profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

Spatial and temporal effects on SARS-CoV-2 contamination of the healthcare environment

Background: The spatial and temporal extent of SARS-CoV-2 environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time. Methods: We sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model. Results: The probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection (floors odds ratio (OR) 67.8 (95% CrI 36.3 to 131); high-touch elevated OR 7.39 (95% CrI 4.31 to 13.1)). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR 1.6 (95% CrI 1.03 to 2.53)). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with duration of the local pandemic wave. Conclusions: Distance from patient’s bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.

Mitigating and adapting to climate change with a taxonomy of smart urban surfaces

Rapid urbanization is replacing natural land with dark, impervious surfaces. This has led to dire urban consequences including rising temperatures and stormwater deluge, resulting in significantly higher energy costs, greater stormwater damage, and associated health and comfort impacts. These issues can be mitigated using smart surfaces, those with high reflectivity and permeability, which can achieve sustainable and regenerative cities. The current literature on the benefits of urban surfaces is very segmented, focusing on either one specific surface type or one property of surfaces. A smart surface taxonomy with correlated heat, and water metrics has been developed to fill this gap. A range of city surfaces in three broad categories - roofs, streets and sidewalks, and parking lots - have been identified with various levels of reflectivity, permeability. Through literature review, the taxonomy reveals surface temperatures that range from 29.7°C for a green roof to 74.3°C for a black roof. Also, the taxonomy reveals Rainfall retention potential ranging from 1.27 mm for impervious pavement to 86.4 mm for bioswales. The development of a smart surface taxonomy with quantified benefits for mitigating or adapting to climate change will be critical for decision-makers to make informed decisions on city surface choices.

Safety Performance Enhancement Scheme for Munition Storages by applying the Concept of Shallow Underground Configurations

Practical demand for the expansion of military ammunition and explosives storage in both volume and number has been increased, but due to the regulations applying on safety distance that require those facilities to be isolated from a civilian presence there are constant complications that arise. Recent incidents include petitions to either alleviate said regulations or relocate several ammunition storage facilities neighboring civilian areas are further development. Two types of underground ammunition storage facilities wold be considered in practice; the first is the tunnel-type which is applicable to areas that have sufficient depth of the cover and the latter is the sub-surface type that retains a sufficient depth of soli layer which can especially be utilized in areas that do not meet clearance requirements nor have geographical limitations. For the sub-surface type storage, there are two construction schemes for construction to meet safety-distance requirements. The existing popular ECMs (Earth Covered Magazines) have shallow soil cover for just plantation camouflage that is not affect the pressure suppression effect due to the internal explosion. Therefore, the scheme of the increasing soil cover depth to some amount, if applicable, pressure and fragment suppression can be achieved. The open-cut method for new construction is easily applied for this purpose in the field. This study addresses the safety distance reduction effect by increasing the soil cover depth on the ECM type storage facility by applying theoretical and numerical analysis.

Reliability Evaluation of the Joint Observation of Cloud Top Height by FY-4A and HIMAWARI-8

It is well known that the measurement of cloud top height (CTH) is important, and a geostationary satellite is an important measurement method. However, it is difficult for a single geostationary satellite to observe the global CTH, so joint observation by multiple satellites is imperative. We used both active and passive sensors to evaluate the reliability of joint observation of geostationary satellites, which includes consistency and accuracy. We analyzed the error of CTH of FY-4A and HIMAWARI-8 and the consistency between the two satellites and conducted research on the problem of missing measurement (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has CTH data, but FY-4A/HIMAWARI-8 does not) of the two satellites. The results show that FY-4A and HIMAWARI-8 have good consistency and can be jointly observed, but the measurement of CTH of FY-4A and HIMAWARI-8 has large errors, and the error of FY-4A is greater than that of HIMAWIRI-8. The error of CTH is affected by the CTH, cloud optical thickness (COT) and cloud type, and the consistency between the two satellites is mainly affected by the cloud type. FY-4A and HIMAWARI-8 have the problem of missing measurement. The missing rate of HIMAWARI-8 is greater than that of FY-4A, and the missing rate is not affected by the CTH, COT and surface type. Therefore, although FY-4A and HIMAWARI-8 have good consistency, the error of CTH and the problem of missing measurement still limit the reliability of their joint observation.

Quality Scoring of the Fengyun 4A Clear Sky Radiance Product

The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.