Search

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.

Abstract We present average ground‐surface velocities and strain rates for the 1.7 million km2 area of Iran, from the joint inversion of InSAR‐derived displacements and GNSS data. We generate interferograms from 7 years of Sentinel‐1 radar acquisitions, correct for tropospheric noise using the GACOS system, estimate average velocities using LiCSBAS time‐series analysis, tie this into a Eurasia‐fixed reference frame, and perform a decomposition to estimate East and Vertical velocities at 500 m spacing. Our InSAR‐GNSS velocity fields reveal predominantly diffuse crustal deformation, with localized interseismic strain accumulation along the North Tabriz, Main Kopet Dagh, Main Recent, Sharoud, and Doruneh faults. We observe signals associated with recent groundwater subsidence, co‐ and postseismic deformation, active salt diaprism, and sediment motion. We derive high‐resolution strain rate estimates on a country‐ and fault‐scale, and discuss the difficulties of mapping diffuse strain rates in areas with abundant non‐tectonic and anthropogenic signals.