Front Mudguard for Xiaomi for m365 PRO for 1s Models Front(Blue)

• Perfect Compatibility: Specifically designed for for xiaomi for mi Electric Scooter, PRO, for m365, and for 1s models, a precise fit and seamless integration with your existing front wheel assembly.
• Effective for splash Protection: Engineered to deflect water, mud, and debris away from the , keeping you cleaner and safer during wet or messy commuting conditions.
• for premium ABS Material: Constructed from high-quality, weather-resistant ABS plastic that offers excellent durability and structural integrity while weighing only 48.6g to minimize burden.
• Stylish & Lightweight: Features a smooth for surface finish that enhances the visual appeal of your scooter without adding unnecessary weight, available in multiple colors to match your style.
• Easy Installation: Designed for quick and hassle-free setup on the front fork, allowing you to upgrade your ride's protection immediately with no complex tools or modifications required.

Features: * Compatible Design: Designed specifically for for xiaomi, PRO, for for m365, and for for 1s scooter models, easy to install and perfectly compatible. * ABS Material: Made of high-quality ABS plastic, offering excellent performance and strong weather resistance. * Lightweight Design: Weighing only 48.6g, it minimizes extra burden while maintaining structural integrity. * Effective for for splash Protection: Effectively protects riders from water and debris, providing a cleaner and safer riding experience. * Stylish Appearance: Smooth for for surface complements the scooter design, enhancing visual appeal. Specifications: * Product Name: Front Mudguard (for front wheel) * Material: ABS Plastic * Colors: Silver, Green, Blue, Pink, Gray * Size: As shown in the picture Package Contents: 1* Front Mudguard (for front wheel) Note: 1. Due to factors such as monitor brightness and ambient light, the actual color of the product may differ slightly from the image displayed on the website. 2. Please allow for slight manual measurement errors in the data.