Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter.

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution⁻based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements

Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.

BACKGROUND: Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations. METHODS: Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution. RESULTS: Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects. CONCLUSIONS: Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.This project is funded under the Newton Fund Programme awarded by Natural Environmental Research Council (NERC Grant NE/N007018/1) with support from Medical Research Council (MRC) and by the National Natural Science Foundation of China (NSFC Grant 81571130100). The NSFC funding is mainly used to support the field work in China, and NERC funding is mainly used for coordination and the further analysis

A new processing scheme for ultra-high resolution direct infusion mass spectrometry data

High resolution, high accuracy mass spectrometry is widely used to characterise environmental or biological samples with highly complex composition enabling the identification of chemical composition of often unknown compounds. Despite instrumental advancements, the accurate molecular assignment of compounds acquired in high resolution mass spectra remains time consuming and requires automated algorithms, especially for samples covering a wide mass range and large numbers of compounds. A new processing scheme is introduced implementing filtering methods based on element assignment, instrumental error, and blank subtraction. Optional post-processing incorporates common ion selection across replicate measurements and shoulder ion removal. The scheme allows both positive and negative direct infusion electrospray ionisation (ESI) and atmospheric pressure photoionisation (APPI) acquisition with the same programs. An example application to atmospheric organic aerosol samples using an Orbitrap mass spectrometer is reported for both ionisation techniques resulting in final spectra with 0.8% and 8.4% of the peaks retained from the raw spectra for APPI positive and ESI negative acquisition, respectively.This work was supported by the European Research Council (ERC starting grant 279405) and by the U.K. Natural Environment Research Council (NERC grant NE/H52449X/1). ATZ thanks the Natural Sciences and Engineering Research Council of Canada, the Sir Winston Churchill Society of Edmonton, and the Cambridge Trust for PhD funding. IK was supported by a M. Curie Intra-European fellowship (project no. 254319

Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors.

The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM2.5 and PM10 were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 μg/m3 and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 μg/m3 for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R2) values of 0.08 for PM2.5 and 0.03 for PM10 during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Spatiotemporal distribution of pollutants and impact of local meteorology on source influence on pollutants' level in a traffic air-shed in Lagos megacity, Nigeria.

Pollution from vehicular emissions is a major cause of poor air quality observed in many urban and semi-urban towns and cities. As such, this study was conducted to assess air quality and the spatiotemporal distribution of vehicular and traffic-related pollutants in several air sheds of Lagos megacity, the economic nerve centre of Nigeria. A setup of low-cost air quality sensors comprising five (5) units was deployed between November 2018 and February 2019 within traffic corridors in the heart of the city. Diurnal variation of pollutants indicated that carbon dioxide (CO2) peaked during the early hours of the day, total oxide (Ox = NO2+O3) peaked at mid-day while carbon monoxide (CO) had two distinct peaks which correspond to morning and evening rush hours. Nitrogen dioxide (NO2) concentration peaked during evening hours. Average concentrations are NO2 (97.1 ± 9.7) ppb, Ox (78.6 ± 27.2) ppb, CO2 (450.1 ± 31.2) ppm, and CO (2285.63 ± 743.7) ppb. Average concentrations of pollutants were above thresholds set by the World Health Organization (WHO) except for NO2 which was within the range permissible limits. The implication of this is that the atmosphere is polluted due to elevated concentrations of airborne pollutants, an indication which is of both health and environmental concern. The air quality index (AQI) indicates that the quality of ambient air varies from good to very unhealthy for Ox, and unhealthy to very unhealthy for CO, while AQI for PM2.5 and PM10 showed hazardous at all the sampling locations except at UNILAG where it is unhealthy for the sensitive group. For all of the sampling sites, conditional bivariate probability function (CBPF) plots show a significant agreement with the location of known pollution sources

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area.

Funder: Valhalla FoundationFunder: High Meadows FoundationFunder: Children's Investment Fund FoundationFunder: Clean Air FundWe performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO2. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm2 cm-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm2 cm-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO2) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM2.5

Research data supporting "Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments"

Measurements of air pollution collected with novel sensing technologies that now offer the potential to monitor detailed personal exposure during daily life at population scale, thanks to their significantly reduced cost, smaller size and fast-response: This data was collected with a state-of-the-art personal air quality monitor (PAM) and aims to characterise the performance of this specific configuration with currently available sensor variants for specific environmental conditions, as a necessary precursor to a series of papers on exposure and health impacts using these PAMs. More specifically, the PAM has already been deployed to participants of two large cardio-pulmonary cohorts in China AIRLESS- Theme 3 APHH project) and the UK (COPE) and in a number of smaller international pilot projects. This dataset was collected to assess the performance of the PAM sensors in well-characterised outdoor, indoor and commuting microenvironments across seasons and diverse environments. Measurements of gaseous pollutants were collected with commercially available electrochemical sensors and measurements of particulate matter have been collected with a miniaturised optical particle counter. See the README file for a detailed description of each data file

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract.&amp;lt;/strong&amp;gt; The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt;), carbon monoxide (CO), ozone (O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed excellent agreement with standard instrumentation in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at large scale to investigate the underlying mechanisms of the effects of air pollution on health.&amp;lt;/p&amp;gt; </jats:p

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially