Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use.

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7-4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from -0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from -1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0-3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm.

Noninvasive assessment of liver function reserve with fluorescent dosimetry of indocyanine green.

Using in vivo multiphoton fluorescent dosimetry, we demonstrate that the clearance dynamics of Indocyanine Green (ICG) in the blood can quickly reveal liver function reserve. In normal rats, the ICG retention rate was below 10% at the 15-minute post-administration; While in the rat with severe hepatocellular carcinoma (HCC), the 15-minute retention rate is over 40% due to poor liver metabolism. With a 785 nm CW laser, the fluorescence dosimeter can evaluate the liver function reserve at a 1/10 clinical dosage of ICG without any blood sampling. In the future, this low-dosage ICG 15-minute retention dosimetry can be applied for the preoperative assessment of hepatectomy or timely perioperative examination.

Development and validation of a Modified Patient-Generated Subjective Global Assessment as a nutritional assessment tool in cancer patients.

BACKGROUND: Completing Patient-Generated Subjective Global Assessment (PG-SGA) questionnaires is time consuming. This study aimed to develop and validate an easy-to-use modified PG-SGA (mPG-SGA) for cancer patients. METHODS: Seventy professionals assessed the content validity, comprehensibility, and difficulty of the full PG-SGA. A survey including the PG-SGA and other questionnaires was completed by 34 071 adult hospitalized cancer patients with first cancer diagnosis or recurrent disease with any tumour comorbidities from the INSCOC study. Among them, 1558 patients were followed for 5 years after admission. Reliability and rank correlation were estimated to assess the consistency between PG-SGA items and to select mPG-SGA items. The external and internal validity, test-retest reliability, and predictive validity were tested for the mPG-SGA via comparison with both the PG-SGA and abridged PG-SGA (abPG-SGA). RESULTS: After deleting items that more than 50% of professionals considered difficult to evaluate (Worksheet 4) and items with an item-total correlation <0.1, the mPG-SGA was constructed. Nutritional status was categorized using mPG-SGA scores as well-nourished (0 points) or mildly (1-2 points), moderately (3-6 points), or severely malnourished (≥7 points) based on the area under curve (0.962, 0.989, and 0.985) and maximal sensitivity (0.924, 0.918, and 0.945) and specificity (1.000, 1.000, and 0.938) of the cut-off scores. The external and internal validity and test-retest reliability were good. Significant median overall survival differences were found among nutritional status groups categorized by the mPG-SGA: 24, 18, 14, and 10 months for well-nourished, mildly malnourished, moderately malnourished, and severely malnourished, respectively (all Ps < 0.05). Neither the PG-SGA nor the abridged PG-SGA could discriminate the median overall survival differences between the well-nourished and mildly malnourished groups. CONCLUSIONS: We systematically developed and validated the mPG-SGA as an easier-to-use nutritional assessment tool for cancer patients. The mPG-SGA appears to have better predictive validity for survival than the PG-SGA and abridged PG-SGA.

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2.

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3-0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO2) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2-3-year lag. However, a timely assessment of COVID-19's impact on provincial CO2 emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO2 (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO2 (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of -22.3% in freight transport and -59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers.

Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing.

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached -24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary.

Determinants and nutritional assessment value of hand grip strength in patients hospitalized with cancer.

BACKGROUND AND OBJECTIVES: Hand grip strength (HGS) has emerged as a predictor of the nutritional status. However, many factors may modify the malnutrition-HGS association. This study explored the nutritional assessment value and determinants of HGS in patients hospitalized with cancer. METHODS AND STUDY DESIGN: In this multicenter, retrospective, observational study (11,314 patients), the Receiver operator characteristic curve was used to observe HGS and nutritional status sensitivity/specificity. Sex; age; height; weight; mid-upper arm circumference (MAMC); Patient-Generated Subjective Global Assessment (PG-SGA) score; Karnofsky score; physical function (PF) domain; cognitive function (CF) domain; global health and quality of life (QL) domain of EORTC QLQ-C30 (a quality of life instrument designed by the European Organization for Research and Treatment of Cancer); and albumin, prealbumin, and hemoglobin levels were included in a Stepwise analysis model to identify the factors influencing HGS. RESULTS: HGS showed a very low diagnostic value and accuracy for identifying severe malnourishment (area under the curve, 0.615-0.640; p<0.01). HGS positively correlated with sex; height; weight; MAMC; Karnofsky score; QL, PF, and CF domains; and hemoglobin and prealbumin levels (Beta= 0.02-0.42, p<=0.05), and negatively with age (Beta=-0.19, p<0.01). However, the PG-SGA score was excluded because of its very limited contribution to HGS variability. CONCLUSIONS: HGS is a mutifactorial index. The use of HGS cutoff values to identify malnutrition is markedly challenging. Thus, HGS may be of limited use as a predictor of nutritional status.

Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor.

Non-dispersive infrared (NDIR) sensors are a low-cost way to observe carbon dioxide concentrations in air, but their specified accuracy and precision are not sufficient for some scientific applications. An initial evaluation of six SenseAir K30 carbon dioxide NDIR sensors in a lab setting showed that without any calibration or correction, the sensors have an individual root mean square error (RMSE) between ~5 and 21 parts per million (ppm) compared to a research-grade greenhouse gas analyzer using cavity enhanced laser absorption spectroscopy. Through further evaluation, after correcting for environmental variables with coefficients determined through a multivariate linear regression analysis, the calculated difference between the each of six individual K30 NDIR sensors and the higher-precision instrument had an RMSE of between 1.7 and 4.3 ppm for 1 min data. The median RMSE improved from 9.6 for off-the-shelf sensors to 1.9 ppm after correction and calibration, demonstrating the potential to provide useful information for ambient air monitoring.

Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems.

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as UN population projections. This makes current models likely to miss important feedbacks in the real Earth-Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth-Human system models for devising effective science-based policies and measures to benefit current and future generations.

Robust assessment of the expansion and retreat of Mediterranean climate in the 21st century.

The warm-temperate regions of the globe characterized by dry summers and wet winters (Mediterranean climate; MED) are especially vulnerable to climate change. The potential impact on water resources, ecosystems and human livelihood requires a detailed picture of the future changes in this unique climate zone. Here we apply a probabilistic approach to quantitatively address how and why the geographic distribution of MED will change based on the latest-available climate projections for the 21st century. Our analysis provides, for the first time, a robust assessment of significant northward and eastward future expansions of MED over both the Euro-Mediterranean and western North America. Concurrently, we show a significant 21st century replacement of the equatorward MED margins by the arid climate type. Moreover, future winters will become wetter and summers drier in both the old and newly established MED zones. Should these projections be realized, living conditions in some of the most densely populated regions in the world will be seriously jeopardized.