Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil

Noise pollution is becoming a critical health risk for city life. In 2020, the COVID-19 pandemic forced many cities to implement several mobility restrictions. These restrictions changed human activity patterns and decreased the noise levels and noise pollution that often affect urban settings. As the number of infections decreased, so did the outdoor activities, influencing the population's perception of noise. This paper aims to evaluate the changes in noise levels associated with mobility restrictions between 2020 and 2021 in Guayaquil, Ecuador. This study used crowdsourcing with the help of smartphones and mobile applications to collect geo-referenced environmental noise data. The data was used to generate noise maps in different time frames. Finally, noise level maps were created using GIS-based tools to identify the urban areas that experienced the highest noise level variation during the study period. The results show that the most significant noise increase occurred at night. Furthermore, when analyzing noise level changes in different urban areas, the western area of Guayaquil was the one that experienced the most significant noise level variation. Findings inform the perception of noise pollution and could potentially serve as a reference for decision-makers during the proposal of public policies that ensure a better quality of life for its citizens. © 2023 The Authors

COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of the COVID-19 pandemic. In this paper, we analyzed the COVID-19 dataset which includes the number of biweekly infected cases registered in Ontario from March 2020 to the end of June 2021. We made use of Bayesian Spatial–temporal models and Area-to-point (ATP) and Area-to-area (ATA) Poisson Kriging models. With the Bayesian models, spatial–temporal effects and government intervention effects on infection risk are considered while the ATP Poisson Kriging models are used to display the spread of the pandemic over space. © 2023 by the authors.

Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico

The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.

Real-Time Temperature Distribution Monitoring in Chinese Solar Greenhouse Using Virtual LAN

The internal air temperature of Chinese solar greenhouse (CSG) has the problem of uneven spatial and temporal distribution. To determine temperature distribution at different locations, we designed a greenhouse temperature real-time monitoring system based on virtual local area network (VLAN) and estimate, including interpolation estimation module, data acquisition, and transmission module. The temperature data were obtained from 24 sensors, and the Ordinary Kriging algorithm estimated the temperature distribution of the whole plane according to the data. The results showed that the real-time temperature distribution monitoring method established was fast and robust. In addition, data validity rate for VLAN technology deployed for data transmission was 2.64% higher than that of cellular network technology. The following results are obtained by interpolation estimation of temperature data using gaussian model. The average relative error (ARE) of estimate, mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2) were −0.12 °C, 0.42 °C, 0.56 °C, and 0.9964, respectively. After simple optimization of the number of sensors, the following conclusions are drawn. When the number of sensors were decreased to 12~16, MAE, RMSE, and R2 were 0.40~0.60 °C, 0.60~0.80 °C, and >0.99, respectively. Furthermore, temperature distribution in the greenhouse varied in the east–west and north–south directions and had strong regularity. The calculation speed of estimate interpolation algorithm was 50~150 ms, and greenhouse Temperature Distribution Real-time Monitoring System (TDRMS) realized simultaneous acquisition, processing, and fast estimate.

Statistical assessment of spatio-temporal impact of Covid-19 lockdown on air pollution using different modelling approaches in India, 2019-2020

One of the main contributors to air pollution is particulate matter (PMxy), which causes several Covid-19 related diseases such as respiratory problems and cardiovascular disorders. Therefore, the spatial and temporal trend analysis of particulate matter and the mass concentration of all aerosol particles <= 2.5 mu m in diameter (PM2.5) have become critical to control the risk factors of co-morbidity of a patient. Lockdown plays a significant role in reducing Covid-19 cases as well as air pollution, including particulate matter concentration. This study aims to analyse the effect of the lockdown on controlling air pollution in metropolitan cities in India through various statistical modelling approaches. Most research articles in the literature assume a linear relationship between responses and covariates and take independent and identically distributed error terms in the model, which may not be appropriate for analysing such air pollution data. In this study, a pattern analysis of PM2.5 daily emissions in different main activity zones during 2019 and 2020 was performed. The seasonal effect was also taken into account when measuring the lockdown effect. The PM2.5 values at the unobserved location were predicted using three popular spatial interpolation techniques: (i) inverse distance weight (IDW), (ii) ordinary kriging (OK), and (iii) random forest regression kriging (RFK), and their root mean square error (RMSE) was compared. Subsequently, the spatio-temporal intervention of lock down on air pollution was estimated using the difference-in-difference (DID) estimator. In winter, the transport zones, namely Anand Vihar and ITO airport, were the most affected regions. The northwestern part of Delhi is the most sensitive zone in terms of air pollution. Due to the lockdown, the weekly PM2.5 emission decreased by 62.15%, the mass concentration of all aerosol particles <= 10 mu m in diameter (PM10) decreased by 53.14%, and the air quality index (AQI) improved by 22.40%. A proposal is made to adopt corrective measures to maintain the air pollution index, taking into account the spatial and temporal variability in the responses.

Responses of regional environmental factors to the reference value of neutrophil-lymphocyte ratio in healthy adults

Objective: This paper screened the factors that may influence the spatial differentiation of Neutrophil-to-lymphocyte ratio (NLR) reference values in healthy adults in China and explored the trend of NLR reference values in China. Methods: For this research, we collected the NLR of 162 681 healthy adults from 62 cities in China. Spearman regression analysis was used to analyze the correlation between NLR and 25 geography secondary indexes. We extracted 9 indexes with significant correlation, built a random forest (RF) model, and predicted the country's urban healthy adults' NLR reference value. By using the disjunctive Kriging method, we obtained the geographical distribution of NLR reference value of healthy adults in China. Results: The reference value of NLR of healthy adults in China was significantly correlated with the 9 secondary indexes, namely, altitude, sunshine duration, annual average temperature, annual average relative humidity, annual temperature range, annual average wind speed, content of organic matter in topsoil, cation exchange capacity in topsoil (clay), and total amount of CaSO4 in soil. The geographical distribution of NLR values of healthy adults in China showed a trend of being higher in Southeast China and lower in Northwest China, higher in coastal areas and lower in inland areas. Conclusion: This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of NLR index. A random forest model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the prognostic factors of the novel coronavirus using NLR reference values in different regions.

The Effect of COVID-19 Pandemic on Emitted PM2.5 in Urban Road Networks: Using Loop Data and Kriging Method for Passenger Cars in the Central Part of the City of Lodz

In this paper, the effect of the COVID-19 pandemic on the emission of PM2.5 generated by passenger cars is investigated. First, traffic data collected from the inductive loop sensors is analyzed. Second, the traffic flow for the whole network system is estimated using an isometric transformed network and the Euclidean space, and the representative one is selected. Then, an emission model is presented for measuring the level of PM2.5 emissions by the passenger cars, and the integration process is given. Finally, the model is implemented on the central part of the city of Lodz, and the value of emissions before and after the COVID-19 pandemic is measured. Finally, the outputs and the process of the model calibration are depicted. Results show that before the pandemic, PM2.5 pollution was highly concentrated in the center and peripheral parts of the area under consideration, and it would gradually drop outside rush hours and grow at peak hours. After the lockdown, the pollution load throughout the whole area, and across its central parts in particular, decreased dramatically. Outputs also illustrate that restrictions not only lower the car-induced PM2.5 but also have a significant effect on the impact zones, areas affected by the pollutants. Another finding is that although the COVID-19 outbreak clearly poses a serious threat to life and health, it has had an exceptionally positive impact on the natural environment, becoming an unconventional mechanism for its restoration.

Kriging Prediction for Transmission of SARS-CoV-2 in Portoviejo—Ecuador

Currently, governments apply strict confinements to prevent the spread of SARS-COV-2, in addition, a population is complied the biosafety measures, however, the virus has mutated. When a contagious area is known, that can be helped to define restrictions then the increase is minimized, predictions of contagions are important where the virus spreads. In this work, the simple, ordinary, and universal kriging methods are presented for the prediction of infected and non-infected people in Portoviejo, Ecuador. That is shown that kriging methods are adequately estimated infected and non-infected people in the canton of Portoviejo, the result variable in a PCR test for COVID-19 is considered. The efficiency of the forecasts of the model is compared, reproducing accuracy in the estimates, where the ordinary kriging has best approximation to real data with respect to others methods. Finally, one goodness-of-fit measure to validate the performance of the model is used, obtaining insignificant estimation error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Spatial Accessibility Assessment of Prehospital EMS with a Focus on the Elderly Population: A Case Study in Ningbo, China.

The spatial accessibility of prehospital EMS is particularly important for the elderly population's physiological functions. Due to the recent expansion of aging populations all over the globe, elderly people's spatial accessibility to prehospital EMS presents a serious challenge. An efficient strategy to address this issue involves using geographic information systems (GIS)-based tools to evaluate the spatial accessibility in conjunction with the spatial distribution of aging people, available road networks, and prehospital EMS facilities. This study employed gravity model and empirical Bayesian Kriging (EBK) interpolation analysis to evaluate the elderly's spatial access to prehospital EMS in Ningbo, China. In our study, we aimed to solve the following specific research questions: In the study area, "what are the characteristics of the prehospital EMS demand of the elderly?" "Do the elderly have equal and convenient spatial access to prehospital EMS?" and "How can we satisfy the prehospital EMS demand of an aging population, improve their spatial access to prehospital EMS, and then ensure their quality of life?" The results showed that 37.44% of patients admitted to prehospital EMS in 2020 were 65 years and older. The rate of utilization of ambulance services by the elderly was 27.39 per 1000 elderly residents. Ambulance use by the elderly was the highest in the winter months and the lowest in the spring months (25.90% vs. 22.38%). As for the disease spectrum, the main disease was found to be trauma and intoxication (23.70%). The mean accessibility score was only 1.43 and nearly 70% of demand points had scored lower than 1. The elderly's spatial accessibility to prehospital EMS had a central-outward gradient decreasing trend from the central region to the southeast and southwest of the study area. Our proposed methodology and its spatial equilibrium results could be taken as a benchmark of prehospital care capacity and help inform authorities' efforts to develop efficient, aging-focused spatial accessibility plans.

Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals.

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 µg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 µg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Population-weighted exposure to air pollution and COVID-19 incidence in Germany.

Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air quality. In this study, we investigate whether long-term exposure to air pollution increases the risk of COVID-19 infection in Germany. The results show that nitrogen dioxide (NO 2 ) is significantly associated with COVID-19 incidence, with a 1 µ g  m - 3 increase in long-term exposure to NO 2 increasing the COVID-19 incidence rate by 5.58% (95% credible interval [CI]: 3.35%, 7.86%). This result is consistent across various models. The analyses can be reproduced and updated routinely using public data sources and shared R code.