Confidence in the use of low-cost sensors (LCS), as a viable alternative to expensive research-grade instruments, is increasing for air quality monitoring due to low-budget and ease of deployment. However, numerous studies have suggested significant variations in their performances under varying environmental conditions, therefore highlighting the need of detailed evaluations and careful calibrations prior to their applications for the region of interest. Such studies have been relatively few in India and particularly lacking in the semiarid urban environments of western India. In this regard, we calibrated LCS for measurements of particle size distribution (OPC-N3) and ozone (O-3) (Alphasense OXB4) utilizing reference-grade measurements (GRIMM, Thermo), and have evaluated the performance of LCS over Ahmedabad. For computing PM2.5, the corrections have been derived from the particle mass size distribution, which improved the accuracy significantly compared to the reference measurements (R-2 similar to 0.7, normalized mean absolute bias similar to 29% ). O-3 variability is calibrated using reference O-3 and sensor-measured temperature and relative humidity, with the aid of machine learning. Measurements from the two O-3 sensors showed good intercorrelation and agreement with the reference (R-2 similar to 0.7). Our study fills a gap of calibration and performance evaluation of LCSs in a distinct urban environment of western India and highlights the need for careful corrections in order to have reliable air quality measurements. LCS-based measurements were found to capture typical features of the urban air quality in this region, and therefore can be deployed to quantify trends and to understand the important factors governing aerosols and O-3. The study can serve as a reference for future developments toward the low-cost comprehensive measurements of atmospheric composition, including other key air pollutants.