Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment.

Low-cost sensors can provide inaccurate data as temperature and humidity affect sensor accuracy. Therefore, calibration and data correction are essential to obtain reliable measurements. This article presents a training and testing method used to calibrate a sensor module assembled from SO2 and NO2 electrochemical sensors (Alphasense B4 and B43F) alongside air temperature (T) and humidity (RH) sensors. Field training and testing were conducted in the industrialized coastal area of Quintero Bay, Chile. The raw responses of the electrochemical (mV) and T-RH sensors were subjected to multiple linear regression (MLR) using three data segments, based on either voltage (SO2 sensor) or temperature (NO2). The resulting MLR equations were used to estimate the reference concentration. In the field test, calibration improved the performance of the sensors after adding T and RH in a linear model. The most robust models for NO2 were associated with data collected at T < 10 °C (R2 = 0.85), while SO2 robust models (R2 = 0.97) were associated with data segments containing higher voltages. Overall, this training and testing method reduced the bias due to T and HR in the evaluated sensors and could be replicated in similar environments to correct raw data from low-cost electrochemical sensors. A calibration method based on training and sensor testing after relocation is presented. The results show that the SO2 sensor performed better when modeled for different segments of voltage data, and the NO2 sensor model performed better when calibrated for different temperature data segments.

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile.

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.

Multilocus Sequence Analysis of Clinical "Candidatus Neoehrlichia mikurensis" Strains from Europe.

"Candidatus Neoehrlichia mikurensis" is the tick-borne agent of neoehrlichiosis, an infectious disease that primarily affects immunocompromised patients. So far, the genetic variability of "Ca. Neoehrlichia" has been studied only by comparing 16S rRNA genes and groEL operon sequences. We describe the development and use of a multilocus sequence analysis (MLSA) protocol to characterize the genetic diversity of clinical "Ca. Neoehrlichia" strains in Europe and their relatedness to other species within the Anaplasmataceae family. Six genes were selected: ftsZ, clpB, gatB, lipA, groEL, and 16S rRNA. Each MLSA locus was amplified by real-time PCR, and the PCR products were sequenced. Phylogenetic trees of MLSA locus relatedness were constructed from aligned sequences. Blood samples from 12 patients with confirmed "Ca. Neoehrlichia" infection from Sweden (n = 9), the Czech Republic (n = 2), and Germany (n = 1) were analyzed with the MLSA protocol. Three of the Swedish strains exhibited identical lipA sequences, while the lipA sequences of the strains from the other nine patients were identical to each other. One of the Czech strains had one differing nucleotide in the clpB sequence from the sequences of the other 11 strains. All 12 strains had identical sequences for the genes 16S rRNA, ftsZ, gatB, and groEL. According to the MLSA, among the Anaplasmataceae, "Ca. Neoehrlichia" is most closely related to Ehrlichia ruminantium, less so to Anaplasma phagocytophilum, and least to Wolbachia endosymbionts. To conclude, three sequence types of infectious "Ca. Neoehrlichia" were identified: one in the west of Sweden, one in the Czech Republic, and one spread throughout Europe.

Carbon monoxide concentrations in outdoor wood-fired kitchens in Ouagadougou, Burkina Faso--implications for women's and children's health.

A majority of households in developing countries rely on biomass fuel for cooking, typically burned in open fires or simple stoves. The incomplete combustion of these fuels causes adverse health effects such as respiratory diseases, especially among women and children. However, quantitative data on pollution levels and on associated diseases are limited. We examined cooking habits and self-reported health in 31 households with outdoor open wood fires in Ouagadougou, Burkina Faso, using structured interviews. In eight households, carbon monoxide (CO) was measured using passive sampling. In addition, meteorology and ambient CO concentrations were assessed. The average CO concentration during cooking was 4.3 ppm, with a maximum of 65.3 ppm and minimum of 0.3 ppm (1-min values). A clear daily pattern was observed, with relatively low concentrations during the day and high during the evening, occasionally exceeding the World Health Organization 1- and 8-h guidelines when the air stabilised. On average, CO concentrations were 43 % higher in kitchens located in closed yards than in those located in open yards, showing that fireplace location affected the levels. Eye irritation and coughing among women and children were reported by 30 % of the households. Based on previously reported relations between CO concentrations and fine particles (<2.5 µm), the exposure to biomass smoke appears to be high enough to pose a considerable health risk among women and children in households with outdoor open wood fires. The results suggest that burning should be limited between sunset and dawn and in areas with limited ventilation to reduce pollutions levels.